{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis - Marketing Campaigns Analysis\n",
    "\n",
    "In this notebook we will explore how to perform **Non Visual** as well as **Visual Analysis** on the Marketing Dataset. \n",
    "\n",
    "## Getting Started\n",
    "\n",
    "**About the Use Case**  \n",
    "Consider a well-established company operating in the retail food sector. Presently they have around several hundred thousands of registered customers and serve almost one million consumers a year. They sell products from 5 major categories: wines, rare meat products, exotic fruits, specially prepared fish and sweet products. These can further be divided into gold and regular products. The customers can order and acquire products through 3 sales channels: physical stores, catalogs and the company's website. Globally, the company had solid revenues and a healthy bottom line in the past 3 years, but the profit growth perspectives for the next 3 years are not promising... For this reason, several strategic initiatives are being considered to reverse this situation. One is to improve the performance of marketing activities, with a special focus on marketing campaigns.\n",
    "\n",
    "\n",
    "**The Marketing Department**  \n",
    "The marketing department was pressured to spend its annual budget more wisely. The CMO perceives the importance of having a more quantitative approach when making decisions, the reason why a small team of data scientists was hired with a clear objective in mind: to build a predictive model which will support direct marketing initiatives. Desirably, the success of these activities will prove the value of the approach and convince the more skeptical within the company.\n",
    "\n",
    "**The Objective**  \n",
    "The objective of the team is to build a predictive model that will produce the highest profit for the next direct marketing campaign, scheduled for the next month. The new campaign, sixth, aims at selling a new gadget to the Customer Database. \n",
    "\n",
    "To build the model, a pilot campaign involving 2240 customers was carried out. The customers were selected at random and contacted by phone regarding the acquisition of the gadget. During the following months, customers who bought the offer were properly labeled. The total cost of the sample campaign was 6720MU and the revenue generated by the customers who accepted the offer was 3674MU. Globally the campaign had a profit of -3046MU. The success rate of the campaign was 15%. \n",
    "\n",
    "The objective of the team is to develop a model that predicts customer behavior and to apply it to the rest of the customer base. Hopefully the model will allow the company to cherry pick the customers that are most likely to purchase the offer while leaving out the non-respondents, making the next campaign highly profitable. Moreover, other than maximizing the profit of the campaign, the CMO is interested in understanding to study the characteristic features of those customers who are willing to buy the gadget.\n",
    "\n",
    "\n",
    "**Objectives**   \n",
    "\n",
    "Key Objectives are: \n",
    "1. Explore the data – be creative and pay attention to the details. You need to provide the marketing team a better understanding of the characteristic features of respondents; \n",
    "2. Propose and describe a customer segmentation based on customers behaviors; \n",
    "3. Create a predictive model which allows the company to maximize the profit of the next marketing campaign.\n",
    "4. Whatever else you think is necessary.\n",
    "\n",
    "**The Data Dictionary**  \n",
    "The data set contains socio-demographic and firmographic features about 2240 customers who were contacted. Additionally, it contains a flag for those customers who responded to the campaign, by buying the product.\n",
    "<img height=\"400\" width=\"400\" src=\"data/marketing_data_dictionary.png\">\n",
    "\n",
    "\n",
    "**Are you getting confused from the above problem statement? 🤯😰**\n",
    "\n",
    "Don’t worry. I have compiled the major takeaways in a more informative and easily to understand manner. Kindly go through the following carefully.\n",
    "\n",
    "<a href=\"https://docs.google.com/document/d/1ufcHX6SNkk6e9J2i15tUH3CINxGK5VjwsSLILRxPcUM/edit?usp=sharing\">Click here</a> to get the Domain Knowledge Documentation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "y6ly1jb2uyLn"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "id": "3YVBiDNZwgzP",
    "outputId": "8e3751ef-726f-4ba6-dcd2-94e0a72d6642"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Year_Birth</th>\n",
       "      <th>Education</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Income</th>\n",
       "      <th>Kidhome</th>\n",
       "      <th>Teenhome</th>\n",
       "      <th>Dt_Customer</th>\n",
       "      <th>Recency</th>\n",
       "      <th>MntWines</th>\n",
       "      <th>...</th>\n",
       "      <th>NumStorePurchases</th>\n",
       "      <th>NumWebVisitsMonth</th>\n",
       "      <th>AcceptedCmp3</th>\n",
       "      <th>AcceptedCmp4</th>\n",
       "      <th>AcceptedCmp5</th>\n",
       "      <th>AcceptedCmp1</th>\n",
       "      <th>AcceptedCmp2</th>\n",
       "      <th>Response</th>\n",
       "      <th>Complain</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1826</td>\n",
       "      <td>1970</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>$84,835.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6/16/14</td>\n",
       "      <td>0</td>\n",
       "      <td>189</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1961</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>$57,091.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6/15/14</td>\n",
       "      <td>0</td>\n",
       "      <td>464</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10476</td>\n",
       "      <td>1958</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>$67,267.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5/13/14</td>\n",
       "      <td>0</td>\n",
       "      <td>134</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1386</td>\n",
       "      <td>1967</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Together</td>\n",
       "      <td>$32,474.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5/11/14</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>AUS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5371</td>\n",
       "      <td>1989</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>$21,474.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4/8/14</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID  Year_Birth   Education Marital_Status      Income   Kidhome  \\\n",
       "0   1826        1970  Graduation       Divorced  $84,835.00         0   \n",
       "1      1        1961  Graduation         Single  $57,091.00         0   \n",
       "2  10476        1958  Graduation        Married  $67,267.00         0   \n",
       "3   1386        1967  Graduation       Together  $32,474.00         1   \n",
       "4   5371        1989  Graduation         Single  $21,474.00         1   \n",
       "\n",
       "   Teenhome Dt_Customer  Recency  MntWines  ...  NumStorePurchases  \\\n",
       "0         0     6/16/14        0       189  ...                  6   \n",
       "1         0     6/15/14        0       464  ...                  7   \n",
       "2         1     5/13/14        0       134  ...                  5   \n",
       "3         1     5/11/14        0        10  ...                  2   \n",
       "4         0      4/8/14        0         6  ...                  2   \n",
       "\n",
       "   NumWebVisitsMonth  AcceptedCmp3  AcceptedCmp4  AcceptedCmp5  AcceptedCmp1  \\\n",
       "0                  1             0             0             0             0   \n",
       "1                  5             0             0             0             0   \n",
       "2                  2             0             0             0             0   \n",
       "3                  7             0             0             0             0   \n",
       "4                  7             1             0             0             0   \n",
       "\n",
       "   AcceptedCmp2  Response  Complain  Country  \n",
       "0             0         1         0       SP  \n",
       "1             1         1         0       CA  \n",
       "2             0         0         0       US  \n",
       "3             0         0         0      AUS  \n",
       "4             0         1         0       SP  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PATH = \"data/marketing_data.csv\"\n",
    "\n",
    "df = pd.read_csv(PATH)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7YPAXo8e1gP8",
    "outputId": "439e821b-8cfe-454e-8652-f25927d1edb0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2240, 28)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cWkxF1ha2d00",
    "outputId": "869d0c98-e982-4a84-8fca-6d39ce0c496a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Year_Birth', 'Education', 'Marital_Status', ' Income ',\n",
       "       'Kidhome', 'Teenhome', 'Dt_Customer', 'Recency', 'MntWines',\n",
       "       'MntFruits', 'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts',\n",
       "       'MntGoldProds', 'NumDealsPurchases', 'NumWebPurchases',\n",
       "       'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth',\n",
       "       'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1',\n",
       "       'AcceptedCmp2', 'Response', 'Complain', 'Country'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zcRx1dDFJ1Cj"
   },
   "source": [
    "### Renaming the Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "drEJnC6BJrhQ",
    "outputId": "f78ca855-f45b-41da-ae45-cc6d51054819"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Year_Birth', 'Education', 'Marital_Status', 'Income', 'Kidhome',\n",
       "       'Teenhome', 'Dt_Customer', 'Recency', 'MntWines', 'MntFruits',\n",
       "       'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts',\n",
       "       'MntGoldProds', 'NumDealsPurchases', 'NumWebPurchases',\n",
       "       'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth',\n",
       "       'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1',\n",
       "       'AcceptedCmp2', 'Response', 'Complain', 'Country'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = df.columns.str.strip()\n",
    "\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YA8f0nKdJ4v8"
   },
   "source": [
    "### Fixing the Data Types of Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "o-pvvNbqJ0Zk",
    "outputId": "5bc375f3-376f-4380-f550-d89ee93c556c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2240 entries, 0 to 2239\n",
      "Data columns (total 28 columns):\n",
      " #   Column               Non-Null Count  Dtype \n",
      "---  ------               --------------  ----- \n",
      " 0   ID                   2240 non-null   int64 \n",
      " 1   Year_Birth           2240 non-null   int64 \n",
      " 2   Education            2240 non-null   object\n",
      " 3   Marital_Status       2240 non-null   object\n",
      " 4   Income               2216 non-null   object\n",
      " 5   Kidhome              2240 non-null   int64 \n",
      " 6   Teenhome             2240 non-null   int64 \n",
      " 7   Dt_Customer          2240 non-null   object\n",
      " 8   Recency              2240 non-null   int64 \n",
      " 9   MntWines             2240 non-null   int64 \n",
      " 10  MntFruits            2240 non-null   int64 \n",
      " 11  MntMeatProducts      2240 non-null   int64 \n",
      " 12  MntFishProducts      2240 non-null   int64 \n",
      " 13  MntSweetProducts     2240 non-null   int64 \n",
      " 14  MntGoldProds         2240 non-null   int64 \n",
      " 15  NumDealsPurchases    2240 non-null   int64 \n",
      " 16  NumWebPurchases      2240 non-null   int64 \n",
      " 17  NumCatalogPurchases  2240 non-null   int64 \n",
      " 18  NumStorePurchases    2240 non-null   int64 \n",
      " 19  NumWebVisitsMonth    2240 non-null   int64 \n",
      " 20  AcceptedCmp3         2240 non-null   int64 \n",
      " 21  AcceptedCmp4         2240 non-null   int64 \n",
      " 22  AcceptedCmp5         2240 non-null   int64 \n",
      " 23  AcceptedCmp1         2240 non-null   int64 \n",
      " 24  AcceptedCmp2         2240 non-null   int64 \n",
      " 25  Response             2240 non-null   int64 \n",
      " 26  Complain             2240 non-null   int64 \n",
      " 27  Country              2240 non-null   object\n",
      "dtypes: int64(23), object(5)\n",
      "memory usage: 490.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "C0DMwi09J8VU",
    "outputId": "fb777313-599b-4d9c-ad16-3cf2162d13eb"
   },
   "outputs": [],
   "source": [
    "df['Income'] = df['Income'].str.strip().str.replace('$', '', regex=False).str.replace(',', '', regex=False)\n",
    "\n",
    "df['Income'] = df['Income'].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NweJrgjmJ8S7",
    "outputId": "dcf75e1f-4159-4aa5-86d5-834eaef448b7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2240 entries, 0 to 2239\n",
      "Data columns (total 28 columns):\n",
      " #   Column               Non-Null Count  Dtype         \n",
      "---  ------               --------------  -----         \n",
      " 0   ID                   2240 non-null   int64         \n",
      " 1   Year_Birth           2240 non-null   int64         \n",
      " 2   Education            2240 non-null   object        \n",
      " 3   Marital_Status       2240 non-null   object        \n",
      " 4   Income               2216 non-null   float64       \n",
      " 5   Kidhome              2240 non-null   int64         \n",
      " 6   Teenhome             2240 non-null   int64         \n",
      " 7   Dt_Customer          2240 non-null   datetime64[ns]\n",
      " 8   Recency              2240 non-null   int64         \n",
      " 9   MntWines             2240 non-null   int64         \n",
      " 10  MntFruits            2240 non-null   int64         \n",
      " 11  MntMeatProducts      2240 non-null   int64         \n",
      " 12  MntFishProducts      2240 non-null   int64         \n",
      " 13  MntSweetProducts     2240 non-null   int64         \n",
      " 14  MntGoldProds         2240 non-null   int64         \n",
      " 15  NumDealsPurchases    2240 non-null   int64         \n",
      " 16  NumWebPurchases      2240 non-null   int64         \n",
      " 17  NumCatalogPurchases  2240 non-null   int64         \n",
      " 18  NumStorePurchases    2240 non-null   int64         \n",
      " 19  NumWebVisitsMonth    2240 non-null   int64         \n",
      " 20  AcceptedCmp3         2240 non-null   int64         \n",
      " 21  AcceptedCmp4         2240 non-null   int64         \n",
      " 22  AcceptedCmp5         2240 non-null   int64         \n",
      " 23  AcceptedCmp1         2240 non-null   int64         \n",
      " 24  AcceptedCmp2         2240 non-null   int64         \n",
      " 25  Response             2240 non-null   int64         \n",
      " 26  Complain             2240 non-null   int64         \n",
      " 27  Country              2240 non-null   object        \n",
      "dtypes: datetime64[ns](1), float64(1), int64(23), object(3)\n",
      "memory usage: 490.1+ KB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_18936\\2795487963.py:1: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  df['Dt_Customer'] = pd.to_datetime(df['Dt_Customer'])\n"
     ]
    }
   ],
   "source": [
    "df['Dt_Customer'] = pd.to_datetime(df['Dt_Customer'])\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Missing Values**\n",
    "\n",
    "1. **Missing Completely at Random:** Missingness is unrelated to any variables in the dataset. You can treat MCAR missing values by simply removing the rows with missing values or imputing them with statistical measures such as mean, median, or mode.\n",
    "\n",
    "\n",
    "2. **Missing at Random:** Relationship between Missingness and variables but not on the missing values themselves. For eg: Men are more likely to disclose their weight and age than women. Here Weight and Age are MAR on Gender variable. Biasness wil be more if we simply drop the missing values.\n",
    "\n",
    "\n",
    "3. **Missing Not at Random:** Missingness depends on unobserved variables, you may need more complex imputation methods or modeling techniques to handle MNAR. Biasness wil be more if we simply drop the missing values.\n",
    "\n",
    "\n",
    "**Important Note: Consult with subject matter experts to gain insights into the reasons for missingness and assess potential relationships with other variables. You can also implement statistical tests like chi-square test and correlation test to identify these relationships.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Income                 24\n",
       "ID                      0\n",
       "NumDealsPurchases       0\n",
       "Complain                0\n",
       "Response                0\n",
       "AcceptedCmp2            0\n",
       "AcceptedCmp1            0\n",
       "AcceptedCmp5            0\n",
       "AcceptedCmp4            0\n",
       "AcceptedCmp3            0\n",
       "NumWebVisitsMonth       0\n",
       "NumStorePurchases       0\n",
       "NumCatalogPurchases     0\n",
       "NumWebPurchases         0\n",
       "MntGoldProds            0\n",
       "Year_Birth              0\n",
       "MntSweetProducts        0\n",
       "MntFishProducts         0\n",
       "MntMeatProducts         0\n",
       "MntFruits               0\n",
       "MntWines                0\n",
       "Recency                 0\n",
       "Dt_Customer             0\n",
       "Teenhome                0\n",
       "Kidhome                 0\n",
       "Marital_Status          0\n",
       "Education               0\n",
       "Country                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 2216 entries, 0 to 2239\n",
      "Data columns (total 28 columns):\n",
      " #   Column               Non-Null Count  Dtype         \n",
      "---  ------               --------------  -----         \n",
      " 0   ID                   2216 non-null   int64         \n",
      " 1   Year_Birth           2216 non-null   int64         \n",
      " 2   Education            2216 non-null   object        \n",
      " 3   Marital_Status       2216 non-null   object        \n",
      " 4   Income               2216 non-null   float64       \n",
      " 5   Kidhome              2216 non-null   int64         \n",
      " 6   Teenhome             2216 non-null   int64         \n",
      " 7   Dt_Customer          2216 non-null   datetime64[ns]\n",
      " 8   Recency              2216 non-null   int64         \n",
      " 9   MntWines             2216 non-null   int64         \n",
      " 10  MntFruits            2216 non-null   int64         \n",
      " 11  MntMeatProducts      2216 non-null   int64         \n",
      " 12  MntFishProducts      2216 non-null   int64         \n",
      " 13  MntSweetProducts     2216 non-null   int64         \n",
      " 14  MntGoldProds         2216 non-null   int64         \n",
      " 15  NumDealsPurchases    2216 non-null   int64         \n",
      " 16  NumWebPurchases      2216 non-null   int64         \n",
      " 17  NumCatalogPurchases  2216 non-null   int64         \n",
      " 18  NumStorePurchases    2216 non-null   int64         \n",
      " 19  NumWebVisitsMonth    2216 non-null   int64         \n",
      " 20  AcceptedCmp3         2216 non-null   int64         \n",
      " 21  AcceptedCmp4         2216 non-null   int64         \n",
      " 22  AcceptedCmp5         2216 non-null   int64         \n",
      " 23  AcceptedCmp1         2216 non-null   int64         \n",
      " 24  AcceptedCmp2         2216 non-null   int64         \n",
      " 25  Response             2216 non-null   int64         \n",
      " 26  Complain             2216 non-null   int64         \n",
      " 27  Country              2216 non-null   object        \n",
      "dtypes: datetime64[ns](1), float64(1), int64(23), object(3)\n",
      "memory usage: 502.1+ KB\n"
     ]
    }
   ],
   "source": [
    "### For MCAR\n",
    "\n",
    "# Remove rows with missing values\n",
    "df_dropna = df.dropna()\n",
    "\n",
    "df_dropna.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Cannot convert [['Graduation' 'Graduation' 'Graduation' ... 'Graduation' 'Graduation'\n  'PhD']\n ['Divorced' 'Single' 'Married' ... 'Divorced' 'Married' 'Married']] to numeric",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[12], line 4\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m### For MCAR\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \n\u001b[0;32m      3\u001b[0m \u001b[38;5;66;03m# Impute missing values with mean/median\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m df_median_imputed \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mfillna(\u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmedian\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m      6\u001b[0m df_median_imputed\u001b[38;5;241m.\u001b[39mdescribe()\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\core\\frame.py:11351\u001b[0m, in \u001b[0;36mDataFrame.median\u001b[1;34m(self, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[0;32m  11343\u001b[0m \u001b[38;5;129m@doc\u001b[39m(make_doc(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmedian\u001b[39m\u001b[38;5;124m\"\u001b[39m, ndim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m))\n\u001b[0;32m  11344\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmedian\u001b[39m(\n\u001b[0;32m  11345\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m  11349\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m  11350\u001b[0m ):\n\u001b[1;32m> 11351\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mmedian(axis, skipna, numeric_only, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m  11352\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(result, Series):\n\u001b[0;32m  11353\u001b[0m         result \u001b[38;5;241m=\u001b[39m result\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmedian\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\core\\generic.py:11989\u001b[0m, in \u001b[0;36mNDFrame.median\u001b[1;34m(self, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[0;32m  11982\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmedian\u001b[39m(\n\u001b[0;32m  11983\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m  11984\u001b[0m     axis: Axis \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m  11987\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m  11988\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Series \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mfloat\u001b[39m:\n\u001b[1;32m> 11989\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stat_function(\n\u001b[0;32m  11990\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmedian\u001b[39m\u001b[38;5;124m\"\u001b[39m, nanops\u001b[38;5;241m.\u001b[39mnanmedian, axis, skipna, numeric_only, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[0;32m  11991\u001b[0m     )\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\core\\generic.py:11935\u001b[0m, in \u001b[0;36mNDFrame._stat_function\u001b[1;34m(self, name, func, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[0;32m  11931\u001b[0m nv\u001b[38;5;241m.\u001b[39mvalidate_func(name, (), kwargs)\n\u001b[0;32m  11933\u001b[0m validate_bool_kwarg(skipna, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mskipna\u001b[39m\u001b[38;5;124m\"\u001b[39m, none_allowed\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m> 11935\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reduce\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m  11936\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_only\u001b[49m\n\u001b[0;32m  11937\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\core\\frame.py:11207\u001b[0m, in \u001b[0;36mDataFrame._reduce\u001b[1;34m(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)\u001b[0m\n\u001b[0;32m  11203\u001b[0m     df \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mT\n\u001b[0;32m  11205\u001b[0m \u001b[38;5;66;03m# After possibly _get_data and transposing, we are now in the\u001b[39;00m\n\u001b[0;32m  11206\u001b[0m \u001b[38;5;66;03m#  simple case where we can use BlockManager.reduce\u001b[39;00m\n\u001b[1;32m> 11207\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43mblk_func\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m  11208\u001b[0m out \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39m_constructor_from_mgr(res, axes\u001b[38;5;241m=\u001b[39mres\u001b[38;5;241m.\u001b[39maxes)\u001b[38;5;241m.\u001b[39miloc[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m  11209\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m out_dtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m out\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mboolean\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\core\\internals\\managers.py:1459\u001b[0m, in \u001b[0;36mBlockManager.reduce\u001b[1;34m(self, func)\u001b[0m\n\u001b[0;32m   1457\u001b[0m res_blocks: \u001b[38;5;28mlist\u001b[39m[Block] \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m   1458\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m blk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblocks:\n\u001b[1;32m-> 1459\u001b[0m     nbs \u001b[38;5;241m=\u001b[39m \u001b[43mblk\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1460\u001b[0m     res_blocks\u001b[38;5;241m.\u001b[39mextend(nbs)\n\u001b[0;32m   1462\u001b[0m index \u001b[38;5;241m=\u001b[39m Index([\u001b[38;5;28;01mNone\u001b[39;00m])  \u001b[38;5;66;03m# placeholder\u001b[39;00m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\core\\internals\\blocks.py:377\u001b[0m, in \u001b[0;36mBlock.reduce\u001b[1;34m(self, func)\u001b[0m\n\u001b[0;32m    371\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[0;32m    372\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mreduce\u001b[39m(\u001b[38;5;28mself\u001b[39m, func) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Block]:\n\u001b[0;32m    373\u001b[0m     \u001b[38;5;66;03m# We will apply the function and reshape the result into a single-row\u001b[39;00m\n\u001b[0;32m    374\u001b[0m     \u001b[38;5;66;03m#  Block with the same mgr_locs; squeezing will be done at a higher level\u001b[39;00m\n\u001b[0;32m    375\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m--> 377\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    379\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m    380\u001b[0m         res_values \u001b[38;5;241m=\u001b[39m result\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\core\\frame.py:11139\u001b[0m, in \u001b[0;36mDataFrame._reduce.<locals>.blk_func\u001b[1;34m(values, axis)\u001b[0m\n\u001b[0;32m  11137\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39marray([result])\n\u001b[0;32m  11138\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m> 11139\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m op(values, axis\u001b[38;5;241m=\u001b[39maxis, skipna\u001b[38;5;241m=\u001b[39mskipna, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\core\\nanops.py:147\u001b[0m, in \u001b[0;36mbottleneck_switch.__call__.<locals>.f\u001b[1;34m(values, axis, skipna, **kwds)\u001b[0m\n\u001b[0;32m    145\u001b[0m         result \u001b[38;5;241m=\u001b[39m alt(values, axis\u001b[38;5;241m=\u001b[39maxis, skipna\u001b[38;5;241m=\u001b[39mskipna, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m    146\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 147\u001b[0m     result \u001b[38;5;241m=\u001b[39m alt(values, axis\u001b[38;5;241m=\u001b[39maxis, skipna\u001b[38;5;241m=\u001b[39mskipna, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m    149\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\core\\nanops.py:783\u001b[0m, in \u001b[0;36mnanmedian\u001b[1;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[0;32m    781\u001b[0m     inferred \u001b[38;5;241m=\u001b[39m lib\u001b[38;5;241m.\u001b[39minfer_dtype(values)\n\u001b[0;32m    782\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m inferred \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstring\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmixed\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m--> 783\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot convert \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvalues\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m to numeric\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    784\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    785\u001b[0m     values \u001b[38;5;241m=\u001b[39m values\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mf8\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mTypeError\u001b[0m: Cannot convert [['Graduation' 'Graduation' 'Graduation' ... 'Graduation' 'Graduation'\n  'PhD']\n ['Divorced' 'Single' 'Married' ... 'Divorced' 'Married' 'Married']] to numeric"
     ]
    }
   ],
   "source": [
    "### For MCAR\n",
    "\n",
    "# Impute missing values with mean/median\n",
    "df_median_imputed = df.fillna(df.median())\n",
    "\n",
    "df_median_imputed.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Income', 'Recency', 'MntWines', 'MntFruits', 'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts', 'MntGoldProds']\n"
     ]
    }
   ],
   "source": [
    "numerical_features = list(df.select_dtypes(include=['number']).columns)\n",
    "categorical_features = list(df.select_dtypes(include=['object']).columns)\n",
    "\n",
    "numerical_features.remove('ID')\n",
    "\n",
    "num_discrete_features = ['Year_Birth', 'Kidhome', 'Teenhome',\n",
    "                        'NumDealsPurchases', 'NumWebPurchases', 'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth',\n",
    "                        'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1', 'AcceptedCmp2',\n",
    "                        'Response', 'Complain']\n",
    "\n",
    "numerical_features = [feature for feature in numerical_features if feature not in num_discrete_features]\n",
    "\n",
    "print(numerical_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Income</th>\n",
       "      <th>Recency</th>\n",
       "      <th>MntWines</th>\n",
       "      <th>MntFruits</th>\n",
       "      <th>MntMeatProducts</th>\n",
       "      <th>MntFishProducts</th>\n",
       "      <th>MntSweetProducts</th>\n",
       "      <th>MntGoldProds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>52237.975446</td>\n",
       "      <td>49.109375</td>\n",
       "      <td>303.935714</td>\n",
       "      <td>26.302232</td>\n",
       "      <td>166.950000</td>\n",
       "      <td>37.525446</td>\n",
       "      <td>27.062946</td>\n",
       "      <td>44.021875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>25037.955891</td>\n",
       "      <td>28.962453</td>\n",
       "      <td>336.597393</td>\n",
       "      <td>39.773434</td>\n",
       "      <td>225.715373</td>\n",
       "      <td>54.628979</td>\n",
       "      <td>41.280498</td>\n",
       "      <td>52.167439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1730.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>35538.750000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>23.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>51381.500000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>173.500000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>68289.750000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>504.250000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>56.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>666666.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1493.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>259.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>362.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Income      Recency     MntWines    MntFruits  MntMeatProducts  \\\n",
       "count    2240.000000  2240.000000  2240.000000  2240.000000      2240.000000   \n",
       "mean    52237.975446    49.109375   303.935714    26.302232       166.950000   \n",
       "std     25037.955891    28.962453   336.597393    39.773434       225.715373   \n",
       "min      1730.000000     0.000000     0.000000     0.000000         0.000000   \n",
       "25%     35538.750000    24.000000    23.750000     1.000000        16.000000   \n",
       "50%     51381.500000    49.000000   173.500000     8.000000        67.000000   \n",
       "75%     68289.750000    74.000000   504.250000    33.000000       232.000000   \n",
       "max    666666.000000    99.000000  1493.000000   199.000000      1725.000000   \n",
       "\n",
       "       MntFishProducts  MntSweetProducts  MntGoldProds  \n",
       "count      2240.000000       2240.000000   2240.000000  \n",
       "mean         37.525446         27.062946     44.021875  \n",
       "std          54.628979         41.280498     52.167439  \n",
       "min           0.000000          0.000000      0.000000  \n",
       "25%           3.000000          1.000000      9.000000  \n",
       "50%          12.000000          8.000000     24.000000  \n",
       "75%          50.000000         33.000000     56.000000  \n",
       "max         259.000000        263.000000    362.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### For MCAR\n",
    "\n",
    "# Impute missing values with mean/median\n",
    "df_median_imputed = df[numerical_features].fillna(df[numerical_features].median())\n",
    "\n",
    "df_median_imputed.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Income</th>\n",
       "      <th>Recency</th>\n",
       "      <th>MntWines</th>\n",
       "      <th>MntFruits</th>\n",
       "      <th>MntMeatProducts</th>\n",
       "      <th>MntFishProducts</th>\n",
       "      <th>MntSweetProducts</th>\n",
       "      <th>MntGoldProds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>52247.251354</td>\n",
       "      <td>49.109375</td>\n",
       "      <td>303.935714</td>\n",
       "      <td>26.302232</td>\n",
       "      <td>166.950000</td>\n",
       "      <td>37.525446</td>\n",
       "      <td>27.062946</td>\n",
       "      <td>44.021875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>25037.797168</td>\n",
       "      <td>28.962453</td>\n",
       "      <td>336.597393</td>\n",
       "      <td>39.773434</td>\n",
       "      <td>225.715373</td>\n",
       "      <td>54.628979</td>\n",
       "      <td>41.280498</td>\n",
       "      <td>52.167439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1730.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>35538.750000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>23.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>51741.500000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>173.500000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>68289.750000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>504.250000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>56.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>666666.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1493.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>259.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>362.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Income      Recency     MntWines    MntFruits  MntMeatProducts  \\\n",
       "count    2240.000000  2240.000000  2240.000000  2240.000000      2240.000000   \n",
       "mean    52247.251354    49.109375   303.935714    26.302232       166.950000   \n",
       "std     25037.797168    28.962453   336.597393    39.773434       225.715373   \n",
       "min      1730.000000     0.000000     0.000000     0.000000         0.000000   \n",
       "25%     35538.750000    24.000000    23.750000     1.000000        16.000000   \n",
       "50%     51741.500000    49.000000   173.500000     8.000000        67.000000   \n",
       "75%     68289.750000    74.000000   504.250000    33.000000       232.000000   \n",
       "max    666666.000000    99.000000  1493.000000   199.000000      1725.000000   \n",
       "\n",
       "       MntFishProducts  MntSweetProducts  MntGoldProds  \n",
       "count      2240.000000       2240.000000   2240.000000  \n",
       "mean         37.525446         27.062946     44.021875  \n",
       "std          54.628979         41.280498     52.167439  \n",
       "min           0.000000          0.000000      0.000000  \n",
       "25%           3.000000          1.000000      9.000000  \n",
       "50%          12.000000          8.000000     24.000000  \n",
       "75%          50.000000         33.000000     56.000000  \n",
       "max         259.000000        263.000000    362.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### For MCAR - Using sklearn SimpleImputer\n",
    "### Univariate imputer for completing missing values with simple strategies.\n",
    "\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "# Impute missing values with mean, median, or mode based on observed variables\n",
    "imputer = SimpleImputer(strategy='mean')  # Can also use 'median' or 'most_frequent'\n",
    "\n",
    "df_mean_imputed = pd.DataFrame(imputer.fit_transform(df[numerical_features]),\n",
    "                               columns=df[numerical_features].columns)\n",
    "\n",
    "df_mean_imputed.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Income</th>\n",
       "      <th>Recency</th>\n",
       "      <th>MntWines</th>\n",
       "      <th>MntFruits</th>\n",
       "      <th>MntMeatProducts</th>\n",
       "      <th>MntFishProducts</th>\n",
       "      <th>MntSweetProducts</th>\n",
       "      <th>MntGoldProds</th>\n",
       "      <th>Year_Birth</th>\n",
       "      <th>Kidhome</th>\n",
       "      <th>...</th>\n",
       "      <th>Marital_Status_Widow</th>\n",
       "      <th>Marital_Status_YOLO</th>\n",
       "      <th>Country_AUS</th>\n",
       "      <th>Country_CA</th>\n",
       "      <th>Country_GER</th>\n",
       "      <th>Country_IND</th>\n",
       "      <th>Country_ME</th>\n",
       "      <th>Country_SA</th>\n",
       "      <th>Country_SP</th>\n",
       "      <th>Country_US</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
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       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>...</td>\n",
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       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>52229.343452</td>\n",
       "      <td>49.109375</td>\n",
       "      <td>303.935714</td>\n",
       "      <td>26.302232</td>\n",
       "      <td>166.950000</td>\n",
       "      <td>37.525446</td>\n",
       "      <td>27.062946</td>\n",
       "      <td>44.021875</td>\n",
       "      <td>1968.805804</td>\n",
       "      <td>0.444196</td>\n",
       "      <td>...</td>\n",
       "      <td>0.034375</td>\n",
       "      <td>0.000893</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.119643</td>\n",
       "      <td>0.053571</td>\n",
       "      <td>0.066071</td>\n",
       "      <td>0.001339</td>\n",
       "      <td>0.150446</td>\n",
       "      <td>0.488839</td>\n",
       "      <td>0.048661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>25113.745615</td>\n",
       "      <td>28.962453</td>\n",
       "      <td>336.597393</td>\n",
       "      <td>39.773434</td>\n",
       "      <td>225.715373</td>\n",
       "      <td>54.628979</td>\n",
       "      <td>41.280498</td>\n",
       "      <td>52.167439</td>\n",
       "      <td>11.984069</td>\n",
       "      <td>0.538398</td>\n",
       "      <td>...</td>\n",
       "      <td>0.182231</td>\n",
       "      <td>0.029874</td>\n",
       "      <td>0.257597</td>\n",
       "      <td>0.324616</td>\n",
       "      <td>0.225220</td>\n",
       "      <td>0.248462</td>\n",
       "      <td>0.036580</td>\n",
       "      <td>0.357588</td>\n",
       "      <td>0.499987</td>\n",
       "      <td>0.215206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1730.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1893.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>35349.750000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>23.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1959.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>51371.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>173.500000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>1970.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>68468.250000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>504.250000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>1977.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>666666.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1493.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>259.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>362.000000</td>\n",
       "      <td>1996.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Income      Recency     MntWines    MntFruits  MntMeatProducts  \\\n",
       "count    2240.000000  2240.000000  2240.000000  2240.000000      2240.000000   \n",
       "mean    52229.343452    49.109375   303.935714    26.302232       166.950000   \n",
       "std     25113.745615    28.962453   336.597393    39.773434       225.715373   \n",
       "min      1730.000000     0.000000     0.000000     0.000000         0.000000   \n",
       "25%     35349.750000    24.000000    23.750000     1.000000        16.000000   \n",
       "50%     51371.000000    49.000000   173.500000     8.000000        67.000000   \n",
       "75%     68468.250000    74.000000   504.250000    33.000000       232.000000   \n",
       "max    666666.000000    99.000000  1493.000000   199.000000      1725.000000   \n",
       "\n",
       "       MntFishProducts  MntSweetProducts  MntGoldProds   Year_Birth  \\\n",
       "count      2240.000000       2240.000000   2240.000000  2240.000000   \n",
       "mean         37.525446         27.062946     44.021875  1968.805804   \n",
       "std          54.628979         41.280498     52.167439    11.984069   \n",
       "min           0.000000          0.000000      0.000000  1893.000000   \n",
       "25%           3.000000          1.000000      9.000000  1959.000000   \n",
       "50%          12.000000          8.000000     24.000000  1970.000000   \n",
       "75%          50.000000         33.000000     56.000000  1977.000000   \n",
       "max         259.000000        263.000000    362.000000  1996.000000   \n",
       "\n",
       "           Kidhome  ...  Marital_Status_Widow  Marital_Status_YOLO  \\\n",
       "count  2240.000000  ...           2240.000000          2240.000000   \n",
       "mean      0.444196  ...              0.034375             0.000893   \n",
       "std       0.538398  ...              0.182231             0.029874   \n",
       "min       0.000000  ...              0.000000             0.000000   \n",
       "25%       0.000000  ...              0.000000             0.000000   \n",
       "50%       0.000000  ...              0.000000             0.000000   \n",
       "75%       1.000000  ...              0.000000             0.000000   \n",
       "max       2.000000  ...              1.000000             1.000000   \n",
       "\n",
       "       Country_AUS   Country_CA  Country_GER  Country_IND   Country_ME  \\\n",
       "count  2240.000000  2240.000000  2240.000000  2240.000000  2240.000000   \n",
       "mean      0.071429     0.119643     0.053571     0.066071     0.001339   \n",
       "std       0.257597     0.324616     0.225220     0.248462     0.036580   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "        Country_SA   Country_SP   Country_US  \n",
       "count  2240.000000  2240.000000  2240.000000  \n",
       "mean      0.150446     0.488839     0.048661  \n",
       "std       0.357588     0.499987     0.215206  \n",
       "min       0.000000     0.000000     0.000000  \n",
       "25%       0.000000     0.000000     0.000000  \n",
       "50%       0.000000     0.000000     0.000000  \n",
       "75%       0.000000     1.000000     0.000000  \n",
       "max       1.000000     1.000000     1.000000  \n",
       "\n",
       "[8 rows x 44 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### For MAR or MNAR\n",
    "### A more sophesticated multivariate approach\n",
    "\n",
    "from sklearn.impute import KNNImputer\n",
    "\n",
    "# Preprocessing\n",
    "cat_encoded_df = pd.get_dummies(df[categorical_features])\n",
    "\n",
    "combined_df = pd.concat([df[numerical_features], df[num_discrete_features], cat_encoded_df], axis=1)\n",
    "\n",
    "# KNN Imputation\n",
    "knn_imputer = KNNImputer(n_neighbors = 3)\n",
    "\n",
    "df_knn_imp = pd.DataFrame(knn_imputer.fit_transform(combined_df),\n",
    "                         columns=combined_df.columns,\n",
    "                         index=combined_df.index)\n",
    "\n",
    "df_knn_imp.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IDnD8MQi3rRO"
   },
   "source": [
    "# Steps Involved in Exploratory Data Analysis\n",
    "\n",
    "1. **Univariate Analysis**  \n",
    "  A. Discrete Data (i.e. Categorical or Numerical Discrete Columns)\n",
    "    - Statistical Non Visual Analysis\n",
    "\t\t- Purpose: Helps us describe and summarize the data\n",
    "\t\t- count, nunique, unique, value_counts\n",
    "\t- Visual Analysis\n",
    "\t\t- Purpose: Helps us understand how the data is distributed and Outliers\n",
    "\t\t- Bar/Count Plot   \n",
    "\n",
    "  B. Continuous Numerical Data (i.e. Real Numerical)\n",
    "    - Statistical Non Visual Analysis\n",
    "\t\t- Purpose: Helps us describe and summarize the data\n",
    "\t\t- min, max, sum, mean, median, var, std, range, iqr\n",
    "\t- Visual Analysis\n",
    "\t\t- Purpose: Helps us understand the Distribution of data and Outliers\n",
    "\t\t- Histogram Plot, KDE Plot and Box Plot\n",
    "\n",
    "2. **Bivariate Analysis (Purpose - Helps identify the relationships)**  \n",
    "  A. Continuous Numerical vs Continuous Numerical Data\n",
    "    - Statistical Non Visual Analysis\n",
    "\t\t- Purpose: Is there any relationship between two variables - Linear or non Linear relationship?\n",
    "\t\t- Pearson Correlation Coefficient\n",
    "\t- Visual Analysis\n",
    "\t\t- Scatter Plot\n",
    "\n",
    "  B. Continuous Numerical vs Discrete Data\n",
    "    - Statistical Non Visual Analysis\n",
    "\t\t- Purpose: How many discrete groups are there and Are the individuals in the groups independent or dependent?\n",
    "\t\t- Compare the Mean, Median, Std of the groups\n",
    "\t- Visual Analysis\n",
    "\t\t- Box Plots and Histogram Plots\n",
    "\n",
    "  C. Discrete vs Discrete Data\n",
    "    - Statistical Non Visual Analysis\n",
    "\t\t- Purpose: Are the individuals in the groups independent or dependent?\n",
    "\t\t- Cross Tabs (i.e. Frequency Tables)\n",
    "\t- Visual Analysis\n",
    "\t\t- Stacked Bar Plot, Unstacked Bar Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I1b62jJtE68p"
   },
   "source": [
    "### 1. Univariate Analysis - Statistical Non Visual Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "tJUyYuo92mLV"
   },
   "outputs": [],
   "source": [
    "discrete_df = df.select_dtypes(include=['object'])\n",
    "\n",
    "numerical_df = df.select_dtypes(include=['int64', 'float64'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "YOM96tenJSo5"
   },
   "outputs": [],
   "source": [
    "def discrete_univariate_analysis(discrete_data):\n",
    "    for col_name in discrete_data:\n",
    "        print(\"*\"*10, col_name, \"*\"*10)\n",
    "        print(discrete_data[col_name].agg(['count', 'nunique', 'unique']))\n",
    "        print('Value Counts: \\n', discrete_data[col_name].value_counts())\n",
    "        print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "l9UHf8bTQSUU",
    "outputId": "956ae35d-e32b-47b7-a489-280415530567"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********** Education **********\n",
      "count                                            2240\n",
      "nunique                                             5\n",
      "unique     [Graduation, PhD, 2n Cycle, Master, Basic]\n",
      "Name: Education, dtype: object\n",
      "Value Counts: \n",
      " Graduation    1127\n",
      "PhD            486\n",
      "Master         370\n",
      "2n Cycle       203\n",
      "Basic           54\n",
      "Name: Education, dtype: int64\n",
      "\n",
      "********** Marital_Status **********\n",
      "count                                                   2240\n",
      "nunique                                                    8\n",
      "unique     [Divorced, Single, Married, Together, Widow, Y...\n",
      "Name: Marital_Status, dtype: object\n",
      "Value Counts: \n",
      " Married     864\n",
      "Together    580\n",
      "Single      480\n",
      "Divorced    232\n",
      "Widow        77\n",
      "Alone         3\n",
      "YOLO          2\n",
      "Absurd        2\n",
      "Name: Marital_Status, dtype: int64\n",
      "\n",
      "********** Country **********\n",
      "count                                     2240\n",
      "nunique                                      8\n",
      "unique     [SP, CA, US, AUS, GER, IND, SA, ME]\n",
      "Name: Country, dtype: object\n",
      "Value Counts: \n",
      " SP     1095\n",
      "SA      337\n",
      "CA      268\n",
      "AUS     160\n",
      "IND     148\n",
      "GER     120\n",
      "US      109\n",
      "ME        3\n",
      "Name: Country, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "discrete_univariate_analysis(discrete_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "Oei5H8nSRP8l"
   },
   "outputs": [],
   "source": [
    "def numerical_univariate_analysis(numerical_data):\n",
    "    for col_name in numerical_data:\n",
    "        print(\"*\"*10, col_name, \"*\"*10)\n",
    "        print(numerical_data[col_name].agg(['min', 'max', 'mean', 'median', 'std']))\n",
    "        print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JcJ8zhkmRjKB",
    "outputId": "72fba132-11d7-40d7-e275-e74b869d9012"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********** ID **********\n",
      "min           0.000000\n",
      "max       11191.000000\n",
      "mean       5592.159821\n",
      "median     5458.500000\n",
      "std        3246.662198\n",
      "Name: ID, dtype: float64\n",
      "\n",
      "********** Year_Birth **********\n",
      "min       1893.000000\n",
      "max       1996.000000\n",
      "mean      1968.805804\n",
      "median    1970.000000\n",
      "std         11.984069\n",
      "Name: Year_Birth, dtype: float64\n",
      "\n",
      "********** Income **********\n",
      "min         1730.000000\n",
      "max       666666.000000\n",
      "mean       52247.251354\n",
      "median     51381.500000\n",
      "std        25173.076661\n",
      "Name: Income, dtype: float64\n",
      "\n",
      "********** Kidhome **********\n",
      "min       0.000000\n",
      "max       2.000000\n",
      "mean      0.444196\n",
      "median    0.000000\n",
      "std       0.538398\n",
      "Name: Kidhome, dtype: float64\n",
      "\n",
      "********** Teenhome **********\n",
      "min       0.000000\n",
      "max       2.000000\n",
      "mean      0.506250\n",
      "median    0.000000\n",
      "std       0.544538\n",
      "Name: Teenhome, dtype: float64\n",
      "\n",
      "********** Recency **********\n",
      "min        0.000000\n",
      "max       99.000000\n",
      "mean      49.109375\n",
      "median    49.000000\n",
      "std       28.962453\n",
      "Name: Recency, dtype: float64\n",
      "\n",
      "********** MntWines **********\n",
      "min          0.000000\n",
      "max       1493.000000\n",
      "mean       303.935714\n",
      "median     173.500000\n",
      "std        336.597393\n",
      "Name: MntWines, dtype: float64\n",
      "\n",
      "********** MntFruits **********\n",
      "min         0.000000\n",
      "max       199.000000\n",
      "mean       26.302232\n",
      "median      8.000000\n",
      "std        39.773434\n",
      "Name: MntFruits, dtype: float64\n",
      "\n",
      "********** MntMeatProducts **********\n",
      "min          0.000000\n",
      "max       1725.000000\n",
      "mean       166.950000\n",
      "median      67.000000\n",
      "std        225.715373\n",
      "Name: MntMeatProducts, dtype: float64\n",
      "\n",
      "********** MntFishProducts **********\n",
      "min         0.000000\n",
      "max       259.000000\n",
      "mean       37.525446\n",
      "median     12.000000\n",
      "std        54.628979\n",
      "Name: MntFishProducts, dtype: float64\n",
      "\n",
      "********** MntSweetProducts **********\n",
      "min         0.000000\n",
      "max       263.000000\n",
      "mean       27.062946\n",
      "median      8.000000\n",
      "std        41.280498\n",
      "Name: MntSweetProducts, dtype: float64\n",
      "\n",
      "********** MntGoldProds **********\n",
      "min         0.000000\n",
      "max       362.000000\n",
      "mean       44.021875\n",
      "median     24.000000\n",
      "std        52.167439\n",
      "Name: MntGoldProds, dtype: float64\n",
      "\n",
      "********** NumDealsPurchases **********\n",
      "min        0.000000\n",
      "max       15.000000\n",
      "mean       2.325000\n",
      "median     2.000000\n",
      "std        1.932238\n",
      "Name: NumDealsPurchases, dtype: float64\n",
      "\n",
      "********** NumWebPurchases **********\n",
      "min        0.000000\n",
      "max       27.000000\n",
      "mean       4.084821\n",
      "median     4.000000\n",
      "std        2.778714\n",
      "Name: NumWebPurchases, dtype: float64\n",
      "\n",
      "********** NumCatalogPurchases **********\n",
      "min        0.000000\n",
      "max       28.000000\n",
      "mean       2.662054\n",
      "median     2.000000\n",
      "std        2.923101\n",
      "Name: NumCatalogPurchases, dtype: float64\n",
      "\n",
      "********** NumStorePurchases **********\n",
      "min        0.000000\n",
      "max       13.000000\n",
      "mean       5.790179\n",
      "median     5.000000\n",
      "std        3.250958\n",
      "Name: NumStorePurchases, dtype: float64\n",
      "\n",
      "********** NumWebVisitsMonth **********\n",
      "min        0.000000\n",
      "max       20.000000\n",
      "mean       5.316518\n",
      "median     6.000000\n",
      "std        2.426645\n",
      "Name: NumWebVisitsMonth, dtype: float64\n",
      "\n",
      "********** AcceptedCmp3 **********\n",
      "min       0.000000\n",
      "max       1.000000\n",
      "mean      0.072768\n",
      "median    0.000000\n",
      "std       0.259813\n",
      "Name: AcceptedCmp3, dtype: float64\n",
      "\n",
      "********** AcceptedCmp4 **********\n",
      "min       0.000000\n",
      "max       1.000000\n",
      "mean      0.074554\n",
      "median    0.000000\n",
      "std       0.262728\n",
      "Name: AcceptedCmp4, dtype: float64\n",
      "\n",
      "********** AcceptedCmp5 **********\n",
      "min       0.000000\n",
      "max       1.000000\n",
      "mean      0.072768\n",
      "median    0.000000\n",
      "std       0.259813\n",
      "Name: AcceptedCmp5, dtype: float64\n",
      "\n",
      "********** AcceptedCmp1 **********\n",
      "min       0.000000\n",
      "max       1.000000\n",
      "mean      0.064286\n",
      "median    0.000000\n",
      "std       0.245316\n",
      "Name: AcceptedCmp1, dtype: float64\n",
      "\n",
      "********** AcceptedCmp2 **********\n",
      "min       0.000000\n",
      "max       1.000000\n",
      "mean      0.013393\n",
      "median    0.000000\n",
      "std       0.114976\n",
      "Name: AcceptedCmp2, dtype: float64\n",
      "\n",
      "********** Response **********\n",
      "min       0.000000\n",
      "max       1.000000\n",
      "mean      0.149107\n",
      "median    0.000000\n",
      "std       0.356274\n",
      "Name: Response, dtype: float64\n",
      "\n",
      "********** Complain **********\n",
      "min       0.000000\n",
      "max       1.000000\n",
      "mean      0.009375\n",
      "median    0.000000\n",
      "std       0.096391\n",
      "Name: Complain, dtype: float64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "numerical_univariate_analysis(numerical_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7T5z3eDMTPRc",
    "outputId": "2a8293fe-dbb8-418e-c5cd-947b2b42b249"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Year_Birth', 'Income', 'Kidhome', 'Teenhome', 'Recency',\n",
       "       'MntWines', 'MntFruits', 'MntMeatProducts', 'MntFishProducts',\n",
       "       'MntSweetProducts', 'MntGoldProds', 'NumDealsPurchases',\n",
       "       'NumWebPurchases', 'NumCatalogPurchases', 'NumStorePurchases',\n",
       "       'NumWebVisitsMonth', 'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5',\n",
       "       'AcceptedCmp1', 'AcceptedCmp2', 'Response', 'Complain'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "z7aW7Q70TDW_",
    "outputId": "2fc3746e-81fc-450c-b62a-56e3e239560a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (2240, 8)\n",
      "Columns: Index(['Income', 'Recency', 'MntWines', 'MntFruits', 'MntMeatProducts',\n",
      "       'MntFishProducts', 'MntSweetProducts', 'MntGoldProds'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "discrete_num_cols = ['ID', 'Year_Birth', 'Kidhome', 'Teenhome',\n",
    "                   'NumDealsPurchases', 'NumWebPurchases', 'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth',\n",
    "                   'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1', 'AcceptedCmp2',\n",
    "                   'Response', 'Complain']\n",
    "numerical_df.drop(columns=discrete_num_cols, axis=1, inplace=True)\n",
    "\n",
    "print('Shape:', numerical_df.shape)\n",
    "print('Columns:', numerical_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "R2WQtg6vTDd2",
    "outputId": "9a3750a1-0fbb-44f3-c2c3-4b632a69a427"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (2240, 16)\n",
      "Columns: Index(['ID', 'Year_Birth', 'Kidhome', 'Teenhome', 'NumDealsPurchases',\n",
      "       'NumWebPurchases', 'NumCatalogPurchases', 'NumStorePurchases',\n",
      "       'NumWebVisitsMonth', 'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5',\n",
      "       'AcceptedCmp1', 'AcceptedCmp2', 'Response', 'Complain'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "discrete_num_df = df[discrete_num_cols]\n",
    "\n",
    "print('Shape:', discrete_num_df.shape)\n",
    "print('Columns:', discrete_num_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jgTVMhz3TDgD",
    "outputId": "a3538d05-67ca-4fcc-bce2-fd014fa59d64"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (2240, 15)\n",
      "Columns: ['Year_Birth', 'Kidhome', 'Teenhome', 'NumDealsPurchases', 'NumWebPurchases', 'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth', 'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1', 'AcceptedCmp2', 'Response', 'Complain']\n"
     ]
    }
   ],
   "source": [
    "discrete_num_df = discrete_num_df.drop(columns=['ID'], axis=1)\n",
    "\n",
    "print('Shape:', discrete_num_df.shape)\n",
    "print('Columns:', list(discrete_num_df.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gGbfuggIJNgV",
    "outputId": "6c81ff3f-26fa-4138-da61-4f1ebd53a734"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********** Income **********\n",
      "min         1730.000000\n",
      "max       666666.000000\n",
      "mean       52247.251354\n",
      "median     51381.500000\n",
      "std        25173.076661\n",
      "Name: Income, dtype: float64\n",
      "\n",
      "********** Recency **********\n",
      "min        0.000000\n",
      "max       99.000000\n",
      "mean      49.109375\n",
      "median    49.000000\n",
      "std       28.962453\n",
      "Name: Recency, dtype: float64\n",
      "\n",
      "********** MntWines **********\n",
      "min          0.000000\n",
      "max       1493.000000\n",
      "mean       303.935714\n",
      "median     173.500000\n",
      "std        336.597393\n",
      "Name: MntWines, dtype: float64\n",
      "\n",
      "********** MntFruits **********\n",
      "min         0.000000\n",
      "max       199.000000\n",
      "mean       26.302232\n",
      "median      8.000000\n",
      "std        39.773434\n",
      "Name: MntFruits, dtype: float64\n",
      "\n",
      "********** MntMeatProducts **********\n",
      "min          0.000000\n",
      "max       1725.000000\n",
      "mean       166.950000\n",
      "median      67.000000\n",
      "std        225.715373\n",
      "Name: MntMeatProducts, dtype: float64\n",
      "\n",
      "********** MntFishProducts **********\n",
      "min         0.000000\n",
      "max       259.000000\n",
      "mean       37.525446\n",
      "median     12.000000\n",
      "std        54.628979\n",
      "Name: MntFishProducts, dtype: float64\n",
      "\n",
      "********** MntSweetProducts **********\n",
      "min         0.000000\n",
      "max       263.000000\n",
      "mean       27.062946\n",
      "median      8.000000\n",
      "std        41.280498\n",
      "Name: MntSweetProducts, dtype: float64\n",
      "\n",
      "********** MntGoldProds **********\n",
      "min         0.000000\n",
      "max       362.000000\n",
      "mean       44.021875\n",
      "median     24.000000\n",
      "std        52.167439\n",
      "Name: MntGoldProds, dtype: float64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "numerical_univariate_analysis(numerical_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UUsJHQueVuYf",
    "outputId": "dc23e59e-099d-4dac-d35d-865252dbaf5d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********** Education **********\n",
      "count                                            2240\n",
      "nunique                                             5\n",
      "unique     [Graduation, PhD, 2n Cycle, Master, Basic]\n",
      "Name: Education, dtype: object\n",
      "Value Counts: \n",
      " Graduation    1127\n",
      "PhD            486\n",
      "Master         370\n",
      "2n Cycle       203\n",
      "Basic           54\n",
      "Name: Education, dtype: int64\n",
      "\n",
      "********** Marital_Status **********\n",
      "count                                                   2240\n",
      "nunique                                                    8\n",
      "unique     [Divorced, Single, Married, Together, Widow, Y...\n",
      "Name: Marital_Status, dtype: object\n",
      "Value Counts: \n",
      " Married     864\n",
      "Together    580\n",
      "Single      480\n",
      "Divorced    232\n",
      "Widow        77\n",
      "Alone         3\n",
      "YOLO          2\n",
      "Absurd        2\n",
      "Name: Marital_Status, dtype: int64\n",
      "\n",
      "********** Country **********\n",
      "count                                     2240\n",
      "nunique                                      8\n",
      "unique     [SP, CA, US, AUS, GER, IND, SA, ME]\n",
      "Name: Country, dtype: object\n",
      "Value Counts: \n",
      " SP     1095\n",
      "SA      337\n",
      "CA      268\n",
      "AUS     160\n",
      "IND     148\n",
      "GER     120\n",
      "US      109\n",
      "ME        3\n",
      "Name: Country, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "discrete_univariate_analysis(discrete_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "p8LSPAt0V1RZ",
    "outputId": "66742a92-2db2-4848-ca5f-6f21afdcbf24"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********** Year_Birth **********\n",
      "count                                                   2240\n",
      "nunique                                                   59\n",
      "unique     [1970, 1961, 1958, 1967, 1989, 1954, 1947, 197...\n",
      "Name: Year_Birth, dtype: object\n",
      "Value Counts: \n",
      " 1976    89\n",
      "1971    87\n",
      "1975    83\n",
      "1972    79\n",
      "1970    77\n",
      "1978    77\n",
      "1965    74\n",
      "1973    74\n",
      "1969    71\n",
      "1974    69\n",
      "1956    55\n",
      "1979    53\n",
      "1958    53\n",
      "1952    52\n",
      "1977    52\n",
      "1959    51\n",
      "1968    51\n",
      "1966    50\n",
      "1954    50\n",
      "1955    49\n",
      "1960    49\n",
      "1982    45\n",
      "1963    45\n",
      "1967    44\n",
      "1962    44\n",
      "1957    43\n",
      "1951    43\n",
      "1964    42\n",
      "1983    42\n",
      "1986    42\n",
      "1980    39\n",
      "1981    39\n",
      "1984    38\n",
      "1961    36\n",
      "1953    35\n",
      "1985    32\n",
      "1989    30\n",
      "1949    30\n",
      "1950    29\n",
      "1988    29\n",
      "1987    27\n",
      "1948    21\n",
      "1990    18\n",
      "1947    16\n",
      "1946    16\n",
      "1991    15\n",
      "1992    13\n",
      "1945     8\n",
      "1944     7\n",
      "1943     7\n",
      "1993     5\n",
      "1995     5\n",
      "1994     3\n",
      "1996     2\n",
      "1893     1\n",
      "1899     1\n",
      "1941     1\n",
      "1940     1\n",
      "1900     1\n",
      "Name: Year_Birth, dtype: int64\n",
      "\n",
      "********** Kidhome **********\n",
      "count           2240\n",
      "nunique            3\n",
      "unique     [0, 1, 2]\n",
      "Name: Kidhome, dtype: object\n",
      "Value Counts: \n",
      " 0    1293\n",
      "1     899\n",
      "2      48\n",
      "Name: Kidhome, dtype: int64\n",
      "\n",
      "********** Teenhome **********\n",
      "count           2240\n",
      "nunique            3\n",
      "unique     [0, 1, 2]\n",
      "Name: Teenhome, dtype: object\n",
      "Value Counts: \n",
      " 0    1158\n",
      "1    1030\n",
      "2      52\n",
      "Name: Teenhome, dtype: int64\n",
      "\n",
      "********** NumDealsPurchases **********\n",
      "count                                                   2240\n",
      "nunique                                                   15\n",
      "unique     [1, 2, 3, 0, 4, 12, 7, 5, 6, 11, 9, 8, 10, 15,...\n",
      "Name: NumDealsPurchases, dtype: object\n",
      "Value Counts: \n",
      " 1     970\n",
      "2     497\n",
      "3     297\n",
      "4     189\n",
      "5      94\n",
      "6      61\n",
      "0      46\n",
      "7      40\n",
      "8      14\n",
      "9       8\n",
      "15      7\n",
      "11      5\n",
      "10      5\n",
      "12      4\n",
      "13      3\n",
      "Name: NumDealsPurchases, dtype: int64\n",
      "\n",
      "********** NumWebPurchases **********\n",
      "count                                                   2240\n",
      "nunique                                                   15\n",
      "unique     [4, 7, 3, 1, 10, 2, 6, 5, 25, 8, 9, 0, 11, 27,...\n",
      "Name: NumWebPurchases, dtype: object\n",
      "Value Counts: \n",
      " 2     373\n",
      "1     354\n",
      "3     336\n",
      "4     280\n",
      "5     220\n",
      "6     205\n",
      "7     155\n",
      "8     102\n",
      "9      75\n",
      "0      49\n",
      "11     44\n",
      "10     43\n",
      "27      2\n",
      "25      1\n",
      "23      1\n",
      "Name: NumWebPurchases, dtype: int64\n",
      "\n",
      "********** NumCatalogPurchases **********\n",
      "count                                                2240\n",
      "nunique                                                14\n",
      "unique     [4, 3, 2, 0, 1, 7, 10, 6, 8, 5, 9, 11, 28, 22]\n",
      "Name: NumCatalogPurchases, dtype: object\n",
      "Value Counts: \n",
      " 0     586\n",
      "1     497\n",
      "2     276\n",
      "3     184\n",
      "4     182\n",
      "5     140\n",
      "6     128\n",
      "7      79\n",
      "8      55\n",
      "10     48\n",
      "9      42\n",
      "11     19\n",
      "28      3\n",
      "22      1\n",
      "Name: NumCatalogPurchases, dtype: int64\n",
      "\n",
      "********** NumStorePurchases **********\n",
      "count                                                2240\n",
      "nunique                                                14\n",
      "unique     [6, 7, 5, 2, 3, 9, 10, 0, 8, 4, 13, 12, 1, 11]\n",
      "Name: NumStorePurchases, dtype: object\n",
      "Value Counts: \n",
      " 3     490\n",
      "4     323\n",
      "2     223\n",
      "5     212\n",
      "6     178\n",
      "8     149\n",
      "7     143\n",
      "10    125\n",
      "9     106\n",
      "12    105\n",
      "13     83\n",
      "11     81\n",
      "0      15\n",
      "1       7\n",
      "Name: NumStorePurchases, dtype: int64\n",
      "\n",
      "********** NumWebVisitsMonth **********\n",
      "count                                                   2240\n",
      "nunique                                                   16\n",
      "unique     [1, 5, 2, 7, 6, 4, 8, 3, 9, 0, 17, 13, 10, 14,...\n",
      "Name: NumWebVisitsMonth, dtype: object\n",
      "Value Counts: \n",
      " 7     393\n",
      "8     342\n",
      "6     340\n",
      "5     281\n",
      "4     218\n",
      "3     205\n",
      "2     202\n",
      "1     153\n",
      "9      83\n",
      "0      11\n",
      "10      3\n",
      "20      3\n",
      "14      2\n",
      "19      2\n",
      "17      1\n",
      "13      1\n",
      "Name: NumWebVisitsMonth, dtype: int64\n",
      "\n",
      "********** AcceptedCmp3 **********\n",
      "count        2240\n",
      "nunique         2\n",
      "unique     [0, 1]\n",
      "Name: AcceptedCmp3, dtype: object\n",
      "Value Counts: \n",
      " 0    2077\n",
      "1     163\n",
      "Name: AcceptedCmp3, dtype: int64\n",
      "\n",
      "********** AcceptedCmp4 **********\n",
      "count        2240\n",
      "nunique         2\n",
      "unique     [0, 1]\n",
      "Name: AcceptedCmp4, dtype: object\n",
      "Value Counts: \n",
      " 0    2073\n",
      "1     167\n",
      "Name: AcceptedCmp4, dtype: int64\n",
      "\n",
      "********** AcceptedCmp5 **********\n",
      "count        2240\n",
      "nunique         2\n",
      "unique     [0, 1]\n",
      "Name: AcceptedCmp5, dtype: object\n",
      "Value Counts: \n",
      " 0    2077\n",
      "1     163\n",
      "Name: AcceptedCmp5, dtype: int64\n",
      "\n",
      "********** AcceptedCmp1 **********\n",
      "count        2240\n",
      "nunique         2\n",
      "unique     [0, 1]\n",
      "Name: AcceptedCmp1, dtype: object\n",
      "Value Counts: \n",
      " 0    2096\n",
      "1     144\n",
      "Name: AcceptedCmp1, dtype: int64\n",
      "\n",
      "********** AcceptedCmp2 **********\n",
      "count        2240\n",
      "nunique         2\n",
      "unique     [0, 1]\n",
      "Name: AcceptedCmp2, dtype: object\n",
      "Value Counts: \n",
      " 0    2210\n",
      "1      30\n",
      "Name: AcceptedCmp2, dtype: int64\n",
      "\n",
      "********** Response **********\n",
      "count        2240\n",
      "nunique         2\n",
      "unique     [1, 0]\n",
      "Name: Response, dtype: object\n",
      "Value Counts: \n",
      " 0    1906\n",
      "1     334\n",
      "Name: Response, dtype: int64\n",
      "\n",
      "********** Complain **********\n",
      "count        2240\n",
      "nunique         2\n",
      "unique     [0, 1]\n",
      "Name: Complain, dtype: object\n",
      "Value Counts: \n",
      " 0    2219\n",
      "1      21\n",
      "Name: Complain, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "discrete_univariate_analysis(discrete_num_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TlQq7vKolU71",
    "outputId": "39129944-cb2b-4224-84ac-419122da822d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2240 entries, 0 to 2239\n",
      "Data columns (total 28 columns):\n",
      " #   Column               Non-Null Count  Dtype         \n",
      "---  ------               --------------  -----         \n",
      " 0   ID                   2240 non-null   object        \n",
      " 1   Year_Birth           2240 non-null   object        \n",
      " 2   Education            2240 non-null   object        \n",
      " 3   Marital_Status       2240 non-null   object        \n",
      " 4   Income               2216 non-null   float64       \n",
      " 5   Kidhome              2240 non-null   object        \n",
      " 6   Teenhome             2240 non-null   object        \n",
      " 7   Dt_Customer          2240 non-null   datetime64[ns]\n",
      " 8   Recency              2240 non-null   int64         \n",
      " 9   MntWines             2240 non-null   int64         \n",
      " 10  MntFruits            2240 non-null   int64         \n",
      " 11  MntMeatProducts      2240 non-null   int64         \n",
      " 12  MntFishProducts      2240 non-null   int64         \n",
      " 13  MntSweetProducts     2240 non-null   int64         \n",
      " 14  MntGoldProds         2240 non-null   int64         \n",
      " 15  NumDealsPurchases    2240 non-null   object        \n",
      " 16  NumWebPurchases      2240 non-null   object        \n",
      " 17  NumCatalogPurchases  2240 non-null   object        \n",
      " 18  NumStorePurchases    2240 non-null   object        \n",
      " 19  NumWebVisitsMonth    2240 non-null   object        \n",
      " 20  AcceptedCmp3         2240 non-null   object        \n",
      " 21  AcceptedCmp4         2240 non-null   object        \n",
      " 22  AcceptedCmp5         2240 non-null   object        \n",
      " 23  AcceptedCmp1         2240 non-null   object        \n",
      " 24  AcceptedCmp2         2240 non-null   object        \n",
      " 25  Response             2240 non-null   object        \n",
      " 26  Complain             2240 non-null   object        \n",
      " 27  Country              2240 non-null   object        \n",
      "dtypes: datetime64[ns](1), float64(1), int64(7), object(19)\n",
      "memory usage: 490.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df[discrete_num_cols] = df[discrete_num_cols].astype('object')\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A511OmOoXqni"
   },
   "source": [
    "### 2. Univariate - Visual Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3FbbwvStX441",
    "outputId": "f00c8ab4-2a86-46be-9ed9-17e410fd8dc6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2240, 28)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 917
    },
    "id": "lCXEREfGV1UB",
    "outputId": "a4eef247-fb84-4bf6-e0bf-cba7bde1a47c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:ylabel='Frequency'>,\n",
       "        <AxesSubplot:ylabel='Frequency'>],\n",
       "       [<AxesSubplot:ylabel='Frequency'>,\n",
       "        <AxesSubplot:ylabel='Frequency'>],\n",
       "       [<AxesSubplot:ylabel='Frequency'>,\n",
       "        <AxesSubplot:ylabel='Frequency'>],\n",
       "       [<AxesSubplot:ylabel='Frequency'>,\n",
       "        <AxesSubplot:ylabel='Frequency'>]], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='hist', subplots=True, layout=(4, 2), figsize=(10, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 986
    },
    "id": "N7RMrmhzV1Y-",
    "outputId": "0f4a967b-9ea6-48c6-efa4-78596f5599eb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Income                 AxesSubplot(0.125,0.712609;0.352273x0.167391)\n",
       "Recency             AxesSubplot(0.547727,0.712609;0.352273x0.167391)\n",
       "MntWines               AxesSubplot(0.125,0.511739;0.352273x0.167391)\n",
       "MntFruits           AxesSubplot(0.547727,0.511739;0.352273x0.167391)\n",
       "MntMeatProducts         AxesSubplot(0.125,0.31087;0.352273x0.167391)\n",
       "MntFishProducts      AxesSubplot(0.547727,0.31087;0.352273x0.167391)\n",
       "MntSweetProducts           AxesSubplot(0.125,0.11;0.352273x0.167391)\n",
       "MntGoldProds            AxesSubplot(0.547727,0.11;0.352273x0.167391)\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='box', subplots=True, layout=(4, 2), figsize=(10, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FY40DrDDi41Q",
    "outputId": "8e27404a-bff4-4189-8d62-0034716a09ae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2203, 28)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.loc[df['Income'] < 100000]\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-oiXRSaJjQ9D",
    "outputId": "53c5600c-7e8c-4105-c8f2-8e48c7e2ed4f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2202, 28)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.loc[(df['MntMeatProducts'] < 1000)]\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FpuNMcAEjQ_Y",
    "outputId": "877b7563-c57b-4bcd-d959-faa2ecbc4c39"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2202, 28)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.loc[(df['MntSweetProducts'] < 200)]\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mw5rNcjDjRBp",
    "outputId": "24257462-50cf-4b0c-9c52-0499d6cfed51"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2199, 28)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.loc[(df['MntGoldProds'] < 250)]\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vmEJdoPOej7P"
   },
   "source": [
    "## 3. Bivariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EaHViz4KiId4"
   },
   "source": [
    "### a. Continuous vs Continuous Numerical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XyqaTwnJVua-",
    "outputId": "96e8e7b3-6692-4494-974a-61a877166111"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Year_Birth', 'Education', 'Marital_Status', 'Income', 'Kidhome',\n",
       "       'Teenhome', 'Dt_Customer', 'Recency', 'MntWines', 'MntFruits',\n",
       "       'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts',\n",
       "       'MntGoldProds', 'NumDealsPurchases', 'NumWebPurchases',\n",
       "       'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth',\n",
       "       'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1',\n",
       "       'AcceptedCmp2', 'Response', 'Complain', 'Country'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 355
    },
    "id": "5G0AZu3Dgvgh",
    "outputId": "d7c5a826-f890-4057-d961-f48eac7ae30b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Income</th>\n",
       "      <th>Recency</th>\n",
       "      <th>MntWines</th>\n",
       "      <th>MntFruits</th>\n",
       "      <th>MntMeatProducts</th>\n",
       "      <th>MntFishProducts</th>\n",
       "      <th>MntSweetProducts</th>\n",
       "      <th>MntGoldProds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.006753</td>\n",
       "      <td>0.734493</td>\n",
       "      <td>0.538823</td>\n",
       "      <td>0.726718</td>\n",
       "      <td>0.554295</td>\n",
       "      <td>0.550854</td>\n",
       "      <td>0.438437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recency</th>\n",
       "      <td>0.006753</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.014196</td>\n",
       "      <td>-0.003562</td>\n",
       "      <td>0.026507</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>0.030501</td>\n",
       "      <td>0.023032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MntWines</th>\n",
       "      <td>0.734493</td>\n",
       "      <td>0.014196</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.385156</td>\n",
       "      <td>0.605566</td>\n",
       "      <td>0.394167</td>\n",
       "      <td>0.395094</td>\n",
       "      <td>0.400691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MntFruits</th>\n",
       "      <td>0.538823</td>\n",
       "      <td>-0.003562</td>\n",
       "      <td>0.385156</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.579928</td>\n",
       "      <td>0.592007</td>\n",
       "      <td>0.578764</td>\n",
       "      <td>0.400677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MntMeatProducts</th>\n",
       "      <td>0.726718</td>\n",
       "      <td>0.026507</td>\n",
       "      <td>0.605566</td>\n",
       "      <td>0.579928</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.607236</td>\n",
       "      <td>0.573704</td>\n",
       "      <td>0.396614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MntFishProducts</th>\n",
       "      <td>0.554295</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>0.394167</td>\n",
       "      <td>0.592007</td>\n",
       "      <td>0.607236</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.593281</td>\n",
       "      <td>0.435438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MntSweetProducts</th>\n",
       "      <td>0.550854</td>\n",
       "      <td>0.030501</td>\n",
       "      <td>0.395094</td>\n",
       "      <td>0.578764</td>\n",
       "      <td>0.573704</td>\n",
       "      <td>0.593281</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.369939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MntGoldProds</th>\n",
       "      <td>0.438437</td>\n",
       "      <td>0.023032</td>\n",
       "      <td>0.400691</td>\n",
       "      <td>0.400677</td>\n",
       "      <td>0.396614</td>\n",
       "      <td>0.435438</td>\n",
       "      <td>0.369939</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Income   Recency  MntWines  MntFruits  MntMeatProducts  \\\n",
       "Income            1.000000  0.006753  0.734493   0.538823         0.726718   \n",
       "Recency           0.006753  1.000000  0.014196  -0.003562         0.026507   \n",
       "MntWines          0.734493  0.014196  1.000000   0.385156         0.605566   \n",
       "MntFruits         0.538823 -0.003562  0.385156   1.000000         0.579928   \n",
       "MntMeatProducts   0.726718  0.026507  0.605566   0.579928         1.000000   \n",
       "MntFishProducts   0.554295  0.000122  0.394167   0.592007         0.607236   \n",
       "MntSweetProducts  0.550854  0.030501  0.395094   0.578764         0.573704   \n",
       "MntGoldProds      0.438437  0.023032  0.400691   0.400677         0.396614   \n",
       "\n",
       "                  MntFishProducts  MntSweetProducts  MntGoldProds  \n",
       "Income                   0.554295          0.550854      0.438437  \n",
       "Recency                  0.000122          0.030501      0.023032  \n",
       "MntWines                 0.394167          0.395094      0.400691  \n",
       "MntFruits                0.592007          0.578764      0.400677  \n",
       "MntMeatProducts          0.607236          0.573704      0.396614  \n",
       "MntFishProducts          1.000000          0.593281      0.435438  \n",
       "MntSweetProducts         0.593281          1.000000      0.369939  \n",
       "MntGoldProds             0.435438          0.369939      1.000000  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 405
    },
    "id": "j3AtGeEGisT6",
    "outputId": "b25db397-8f78-4a0a-d5e7-5d5446cf2f2d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Income', ylabel='MntWines'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter', x='Income', y='MntWines', figsize=(4, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 405
    },
    "id": "GccpQVB_mAFo",
    "outputId": "fde04af4-cd15-443e-ae38-3a1335485c84"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Income', ylabel='MntMeatProducts'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter', x='Income', y='MntMeatProducts', figsize=(4, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rFLJ91GdiWkO"
   },
   "source": [
    "### b. Discrete vs Discrete Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iDaL4nObg6wl",
    "outputId": "e9e149bb-293e-420d-da94-ea4c0223091a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Year_Birth', 'Education', 'Marital_Status', 'Income', 'Kidhome',\n",
       "       'Teenhome', 'Dt_Customer', 'Recency', 'MntWines', 'MntFruits',\n",
       "       'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts',\n",
       "       'MntGoldProds', 'NumDealsPurchases', 'NumWebPurchases',\n",
       "       'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth',\n",
       "       'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1',\n",
       "       'AcceptedCmp2', 'Response', 'Complain', 'Country'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 331
    },
    "id": "H1SkHslwhSQ9",
    "outputId": "a00c1136-06a0-4bf7-b088-74117284beb8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Response</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <th></th>\n",
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       "      <th>Divorced</th>\n",
       "      <td>183</td>\n",
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       "Response          0    1\n",
       "Marital_Status          \n",
       "Absurd            1    1\n",
       "Alone             2    1\n",
       "Divorced        183   47\n",
       "Married         754   98\n",
       "Single          362  105\n",
       "Together        509   58\n",
       "Widow            58   18\n",
       "YOLO              1    1"
      ]
     },
     "execution_count": 36,
     "metadata": {},
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    "pd.crosstab(df['Marital_Status'], df['Response'])"
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       "Response               0         1\n",
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       "Widow           0.026376  0.008186\n",
       "YOLO            0.000455  0.000455"
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       "Response               0         1\n",
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       "Together        0.897707  0.102293\n",
       "Widow           0.763158  0.236842\n",
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       "Response               0         1\n",
       "Marital_Status                    \n",
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       "Response               0         1\n",
       "Marital_Status                    \n",
       "Absurd          0.000535  0.003040\n",
       "Alone           0.001070  0.003040\n",
       "Divorced        0.097861  0.142857\n",
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       "Widow           0.031016  0.054711\n",
       "YOLO            0.000535  0.003040"
      ]
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     "metadata": {},
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   ],
   "source": [
    "pd.crosstab(df['Marital_Status'], df['Response'], normalize='columns')"
   ]
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       "Response               0         1       All\n",
       "Marital_Status                              \n",
       "Absurd          0.000535  0.003040  0.000910\n",
       "Alone           0.001070  0.003040  0.001364\n",
       "Divorced        0.097861  0.142857  0.104593\n",
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   "source": [
    "pd.crosstab(df['Marital_Status'], df['Response'], normalize='columns', margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Marital_Status'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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jKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjMpjOgCyTum3F97X8Sf//VwmJQEA4PYYGQEAAEZRRgAAgFGUEQAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFGUEQAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRGSojY8aMkb+/v9zc3BQUFKR169bd8fUJCQl677335OfnJ1dXVz3yyCOaPHlyhgIDAADHkie9B8yaNUsREREaM2aMateurfHjx6tJkyY6cOCASpUqleYxrVu31m+//aZJkyapbNmyio2NVWJi4n2HBwAAOV+6y0hkZKQ6d+6sLl26SJJGjBihJUuWaOzYsRo8eHCq1y9evFhr1qzR8ePHVbhwYUlS6dKl7/gZCQkJSkhIsD+Pi4tLb0wAAJBDpOsyzdWrV7V9+3YFBwen2B4cHKyNGzemecyCBQtUo0YNffbZZypZsqTKly+vvn376u+//77t5wwePFgFChSwP3x9fdMTEwAA5CDpGhk5d+6ckpKS5OPjk2K7j4+Pzp49m+Yxx48f1/r16+Xm5qb58+fr3LlzCg8P14ULF247b+Sdd95R79697c/j4uIoJAAAOKh0X6aRJJvNluK5ZVmptt2QnJwsm82m6dOnq0CBApKuX+p58cUX9eWXX8rd3T3VMa6urnJ1dc1INAAAkMOk6zJN0aJF5ezsnGoUJDY2NtVoyQ0lSpRQyZIl7UVEkgICAmRZln755ZcMRAYAAI4kXWUkb968CgoK0rJly1JsX7ZsmWrVqpXmMbVr19aZM2f0119/2bcdPnxYTk5OevjhhzMQGQAAOJJ0rzPSu3dvTZw4UZMnT9bBgwfVq1cvRUdHq1u3bpKuz/do166d/fVt2rRRkSJF1LFjRx04cEBr165Vv3791KlTpzQv0QAAgNwl3XNGwsLCdP78eX300UeKiYlRYGCgFi1aJD8/P0lSTEyMoqOj7a/38PDQsmXL9MYbb6hGjRoqUqSIWrdurUGDBmXeWQAAgBwrQxNYw8PDFR4enua+qKioVNsqVqyY6tIOAACAxL1pAACAYZQRAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFGUEQAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFGUEQAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEblMR0gO5R+e+F9HX/SrU3GDx5w8b4+GwAAR/97jJERAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFGUEQAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFGUEQAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFGUEQAAYBRlBAAAGJWhMjJmzBj5+/vLzc1NQUFBWrdu3T0dt2HDBuXJk0dVq1bNyMcCAAAHlO4yMmvWLEVEROi9997Tzp07VbduXTVp0kTR0dF3PO7ixYtq166dnn322QyHBQAAjifdZSQyMlKdO3dWly5dFBAQoBEjRsjX11djx46943Gvvfaa2rRpo5o1a2Y4LAAAcDzpKiNXr17V9u3bFRwcnGJ7cHCwNm7ceNvjvv76ax07dkz9+/e/p89JSEhQXFxcigcAAHBM6Soj586dU1JSknx8fFJs9/Hx0dmzZ9M85siRI3r77bc1ffp05cmT554+Z/DgwSpQoID94evrm56YAAAgB8nQBFabzZbiuWVZqbZJUlJSktq0aaOBAweqfPny9/z+77zzji5evGh/nD59OiMxAQBADnBvQxX/U7RoUTk7O6caBYmNjU01WiJJly5d0rZt27Rz50716NFDkpScnCzLspQnTx4tXbpUzzzzTKrjXF1d5erqmp5oAAAgh0rXyEjevHkVFBSkZcuWpdi+bNky1apVK9Xrvby8tHfvXu3atcv+6NatmypUqKBdu3bpySefvL/0AAAgx0vXyIgk9e7dW23btlWNGjVUs2ZNffXVV4qOjla3bt0kXb/E8uuvv2rq1KlycnJSYGBgiuO9vb3l5uaWajsAAMid0l1GwsLCdP78eX300UeKiYlRYGCgFi1aJD8/P0lSTEzMXdccAQAAuCHdZUSSwsPDFR4enua+qKioOx47YMAADRgwICMfCwAAHBD3pgEAAEZRRgAAgFGUEQAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFGUEQAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgVB7TAfAAG1DgPo69mHk5AAAOjZERAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFGUEQAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFGUEQAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFGUEQAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgVB7TAQAAyDYDCtzHsRczLwdSYGQEAAAYRRkBAABGUUYAAIBRlBEAAGAUE1gB5G5MaASMY2QEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABiVoTIyZswY+fv7y83NTUFBQVq3bt1tXztv3jw1atRIxYoVk5eXl2rWrKklS5ZkODAAAHAs6S4js2bNUkREhN577z3t3LlTdevWVZMmTRQdHZ3m69euXatGjRpp0aJF2r59uxo0aKBmzZpp586d9x0eAADkfOleZyQyMlKdO3dWly5dJEkjRozQkiVLNHbsWA0ePDjV60eMGJHi+aeffqrvv/9eP/zwg6pVq5bmZyQkJCghIcH+PC4uLr0xgYxj3QkAyFbpGhm5evWqtm/fruDg4BTbg4ODtXHjxnt6j+TkZF26dEmFCxe+7WsGDx6sAgUK2B++vr7piQkAAHKQdJWRc+fOKSkpST4+Pim2+/j46OzZs/f0HsOHD9fly5fVunXr277mnXfe0cWLF+2P06dPpycmAADIQTK0HLzNZkvx3LKsVNvSMmPGDA0YMEDff/+9vL29b/s6V1dXubq6ZiQaAADIYdJVRooWLSpnZ+dUoyCxsbGpRktuNWvWLHXu3Flz5sxRw4YN058UAAA4pHRdpsmbN6+CgoK0bNmyFNuXLVumWrVq3fa4GTNmqEOHDvrPf/6j5557LmNJAQCAQ0r3ZZrevXurbdu2qlGjhmrWrKmvvvpK0dHR6tatm6Tr8z1+/fVXTZ06VdL1ItKuXTuNHDlSTz31lH1Uxd3dXQUK3Me3FgAAgENIdxkJCwvT+fPn9dFHHykmJkaBgYFatGiR/Pz8JEkxMTEp1hwZP368EhMT1b17d3Xv3t2+vX379oqKirr/MwAAADlahiawhoeHKzw8PM19txaM1atXZ+QjAABALsG9aQAAgFGUEQAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFGUEQAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFGUEQAAYFQe0wEAPCAGFLiPYy9mXg4AuQ4jIwAAwCjKCAAAMIoyAgAAjKKMAAAAo5jACgC5EROW8QBhZAQAABhFGQEAAEZRRgAAgFGUEQAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFGUEQAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFGUEQAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRGSojY8aMkb+/v9zc3BQUFKR169bd8fVr1qxRUFCQ3NzcVKZMGY0bNy5DYQEAgONJdxmZNWuWIiIi9N5772nnzp2qW7eumjRpoujo6DRff+LECTVt2lR169bVzp079e6776pnz56aO3fufYcHAAA5X7rLSGRkpDp37qwuXbooICBAI0aMkK+vr8aOHZvm68eNG6dSpUppxIgRCggIUJcuXdSpUycNGzbsvsMDAICcL096Xnz16lVt375db7/9dortwcHB2rhxY5rHbNq0ScHBwSm2NW7cWJMmTdK1a9fk4uKS6piEhAQlJCTYn1+8eFGSFBcXl564dskJ8Rk67oY4m3UfB2csc2bgvDOG887IwZx3duO8M4bzzsjBGT/vG39vW9ZdPt9Kh19//dWSZG3YsCHF9k8++cQqX758mseUK1fO+uSTT1Js27BhgyXJOnPmTJrH9O/f35LEgwcPHjx48HCAx+nTp+/YL9I1MnKDzWZL8dyyrFTb7vb6tLbf8M4776h3797258nJybpw4YKKFClyx8/JCnFxcfL19dXp06fl5eWVrZ9tEufNeecGnDfnnRuYPG/LsnTp0iU99NBDd3xduspI0aJF5ezsrLNnz6bYHhsbKx8fnzSPKV68eJqvz5Mnj4oUKZLmMa6urnJ1dU2xrWDBgumJmum8vLxy1W/eGzjv3IXzzl0479zF1HkXKFDgrq9J1wTWvHnzKigoSMuWLUuxfdmyZapVq1aax9SsWTPV65cuXaoaNWqkOV8EAADkLun+Nk3v3r01ceJETZ48WQcPHlSvXr0UHR2tbt26Sbp+iaVdu3b213fr1k2nTp1S7969dfDgQU2ePFmTJk1S3759M+8sAABAjpXuOSNhYWE6f/68PvroI8XExCgwMFCLFi2Sn5+fJCkmJibFmiP+/v5atGiRevXqpS+//FIPPfSQRo0apVatWmXeWWQhV1dX9e/fP9VlI0fHeXPeuQHnzXnnBjnhvG2Wdbfv2wAAAGQd7k0DAACMoowAAACjKCMAAMAoyggAADCKMgIADuzatWvq2LGjjh8/bjoKcFuUEdglJiZq+fLlGj9+vC5duiRJOnPmjP766y/DyQBklIuLi+bPn286hhETJkzQkSNHTMfIdnPmzFHLli0VGBioKlWqqGXLlvr2229Nx7ojvtoracGCBff82ubNm2dhEnNOnTqlkJAQRUdHKyEhQYcPH1aZMmUUERGhK1euaNy4caYjAplm2rRpGjdunE6cOKFNmzbJz89PI0aMkL+/v1q0aGE6Xqbr2LGjqlSpkuKeX7lBxYoVdeTIEfn4+Ojpp59W/fr19fTTT6tixYqmo2WJ5ORk/fOf/9ScOXNUvnx5VaxYUZZl6dChQzp69KheeuklzZgxI9vv8XYvMnSjPEcTGhqa4rnNZktxu+Obf+GSkpKyK1a2evPNN1WjRg3t3r07xT2DXnjhBXXp0sVgsqyRnj+UIyMjszBJ9qpWrdo9/0G0Y8eOLE5jxtixY/Xhhx8qIiJCn3zyif3/6YIFC2rEiBEOWUbKli2rjz/+WBs3blRQUJDy58+fYn/Pnj0NJctahw4d0tmzZ7Vq1SqtWbNGn3/+ucLDw1WsWDHVr19fM2fONB0xU40YMULLly/XggUL9Pzzz6fYt2DBAnXs2FEjR45URESEmYB3wMjILZYvX6633npLn376qWrWrCmbzaaNGzfq/fff16effqpGjRqZjpglihYtqg0bNqhChQry9PTU7t27VaZMGZ08eVKVKlVSfHy86YiZqkGDBimeb9++XUlJSapQoYIk6fDhw3J2dlZQUJBWrlxpImKWGDhwoP2/r1y5ojFjxqhSpUqqWbOmJGnz5s3av3+/wsPDNXjwYFMxs1SlSpX06aefKjQ0NMXv9X379ql+/fo6d+6c6YiZzt/f/7b7bDZbrphPcvnyZa1fv14zZ87UN998I8uylJiYaDpWpnr00UcVERGhTp06pbl/0qRJGjFihPbu3ZvNye6OkZFbREREaNy4capTp459W+PGjZUvXz69+uqrOnjwoMF0WSc5OTnNUZ9ffvlFnp6eBhJlrVWrVtn/OzIyUp6enpoyZYoKFSokSfrjjz/UsWNH1a1b11TELNG/f3/7f3fp0kU9e/bUxx9/nOo1p0+fzu5o2ebEiROqVq1aqu2urq66fPmygURZ78SJE6YjGPHjjz9qzZo1Wr16tXbv3q3KlSurXr16mjt3rsP9vy1JR44cUcOGDW+7v2HDhurRo0c2JkoHCym4ublZe/bsSbV99+7dlpubm4FE2aN169ZW165dLcuyLA8PD+v48ePWpUuXrGeeecbq0KGD4XRZ66GHHrL27duXavvevXutEiVKGEiUPby8vKzDhw+n2n748GHLy8vLQKLsERAQYH333XeWZV3/vX7s2DHLsixr5MiRVvXq1U1Gy3IJCQnWoUOHrGvXrpmOki1sNpvl7e1tDRkyxPrzzz9Nx8lyhQoVsnbv3n3b/Xv27LEKFSqUjYnuHd+mucXjjz+uiIgIxcTE2LedPXtWffr00RNPPGEwWdb6/PPPtWbNGlWqVElXrlxRmzZtVLp0af36668aMmSI6XhZKi4uTr/99luq7bGxsfZvFTkid3d3rV+/PtX29evXy83NzUCi7NGvXz91795ds2bNkmVZ2rJliz755BO9++676tevn+l4WSI+Pl6dO3dWvnz5VLlyZfvNTHv27Kl///vfhtNlncjISNWuXVtDhw5VhQoVFBYWprFjxzrsCHfNmjU1duzY2+7/8ssv7ZdkHzim29CD5vDhw1ZgYKDl4uJiPfLII9Yjjzxiubi4WJUrV7aOHDliOl6Wio+PtyZNmmR1797dev31160JEyZY8fHxpmNlubZt21qlSpWy5syZY50+fdo6ffq0NWfOHKt06dJWu3btTMfLMoMHD7ZcXV2t7t27W9OmTbOmTZtmde/e3XJ3d7cGDx5sOl6W+uqrr6xSpUpZNpvNstls1sMPP2xNnDjRdKws07NnTysoKMhat26dlT9/fvto0Pfff29VrVrVcLrssWfPHmv06NFWy5YtLRcXF6t48eKmI2W6DRs2WC4uLtZLL71k/fTTT9bFixetP//809q0aZP14osvWi4uLtb69etNx0wTE1jTkJycrOXLl+vQoUOyLEuVKlVSw4YNH8ivQ+H+xcfHq2/fvpo8ebKuXbsmScqTJ486d+6soUOHpvrmgSOZPXu2Ro4caf+XYkBAgN588021bt3acLLsce7cOSUnJ8vb29t0lCzl5+enWbNm6amnnkoxaffo0aOqXr264uLiTEfMUjt37tTq1au1atUqrVu3TpcuXVK1atW0detW09Ey3fz58/Xqq6/qwoULKbYXKlRI48ePV6tWrQwluzPKyE0SExPl5uamXbt2KTAw0HScbHf48GGtXr1asbGxSk5OTrHvww8/NJQq+1y+fFnHjh2TZVkqW7asQ5cQ5C758uXTvn37VKZMmRRlZPfu3apXr54uXrxoOmKWaN68udavX6+4uDhVrVpV9evXV/369VWvXj15eXmZjpdl4uPjtWTJEvuCb+XLl1dwcLBcXFwUExOjUqVKGU6YGt+muUmePHnk5+fnsGuJ3MmECRP0+uuvq2jRoipevHiKUSCbzZYrykhMTIxiYmJUr149ubu7y7Ishx8N+/PPP/Xtt9/q+PHj6tu3rwoXLqwdO3bIx8dHJUuWNB0v0+T29VUef/xxLVy4UG+88Yak/187acKECQ/uHIJMUL58eb366qsOXz5ulS9fPr3wwguptu/evVvVq1d/IP+Oo4zc4v3339c777yjb775RoULFzYdJ9sMGjRIn3zyid566y3TUbLd+fPn1bp1a61atUo2m01HjhxRmTJl1KVLFxUsWFDDhw83HTFL7NmzRw0bNlSBAgV08uRJdenSRYULF9b8+fN16tQpTZ061XTETHPrwoa5zeDBgxUSEqIDBw4oMTFRI0eO1P79+7Vp0yatWbPGdLwsM2zYMNMRcI+4THOLatWq6ejRo7p27Zr8/PxSDdU74r+aJMnLy0u7du1SmTJlTEfJdu3atVNsbKwmTpyogIAA+xD20qVL1atXL+3fv990xCzRsGFDVa9eXZ999lmKofuNGzeqTZs2OnnypOmIyER79+7VsGHDtH37diUnJ6t69ep66623VKVKFdPRstSaNWs0bNgwHTx4UDabTQEBAerXr59DrjNyN4yM5CC59V9QL730kpYuXapu3bqZjpLtli5dqiVLlujhhx9Osb1cuXI6deqUoVRZb+vWrRo/fnyq7SVLltTZs2cNJEJWqlKliqZMmWI6Rrb65ptv1LFjR7Vs2VI9e/aUZVnauHGjnn32WUVFRalNmzamI+J/KCO3uHmFytykbNmy+uCDD7R582ZVqVJFLi4uKfY76r0rpOsTV/Ply5dq+7lz5+Tq6mogUfZwc3NL81sUP//8s4oVK2YgUfYoVKhQmvNHbDab3NzcVLZsWXXo0EEdO3Y0kC7rJCcn6+jRo2lOUK9Xr56hVFnrk08+0WeffaZevXrZt7355puKjIzUxx9/7HBlZM+ePXfc//PPP2dTkvTjMg0k5e57Vzz33HOqXr26Pv74Y3l6emrPnj3y8/PTP/7xDyUnJz/wt97OqFdffVW///67Zs+ercKFC2vPnj1ydnZWaGio6tWrpxEjRpiOmCU+//xzffLJJ2rSpImeeOIJWZalrVu3avHixerVq5dOnDihadOmafTo0eratavpuJli8+bNatOmjU6dOqVb/8i32WwP5LB9ZnB1ddX+/ftVtmzZFNuPHj2qwMBAXblyxVCyrOHk5JTqRq833Nj+oP56MzJyixu/mLfzIP4iZobceu8KSRo6dKjq16+vbdu26erVq/rXv/6l/fv368KFC9qwYYPpeFlm2LBhatq0qby9vfX333/r6aef1tmzZ1WzZk198sknpuNlmfXr12vQoEGpLkmOHz9eS5cu1dy5c/Xoo49q1KhRDlNGunXrpho1amjhwoUqUaKEw39L7AZfX1+tWLEiVRlZsWKFfH19DaXKOjn5z3FGRm7x/fffp3h+7do17dy5U1OmTNHAgQPVuXNnQ8myz43fErnlDyzp+pL/Y8eOTTG5r3v37ipRooTpaFlu5cqV2rFjh/2873SjLUfg4eGhXbt2pfmv5apVq+qvv/7SsWPH9OijjzrMjfPy58+v3bt3pzpnRzd27Fj7XWxr1aolm82m9evXKyoqSiNHjtRrr71mOiJuyOYVX3Os6dOnW82bNzcdI0tNmTLFCgwMtFxdXS1XV1erSpUq1tSpU03HAjKVr6+vFRkZmWp7ZGSk5evra1nW9Rtj+vj4ZHe0LNOgQQPrxx9/NB3DiHnz5lm1a9e2ChcubBUuXNiqXbu2/UaJjmrLli1Wr169rOeee856/vnnrV69ellbt241HeuOGBm5R472L6VbRUZG6oMPPlCPHj1Uu3ZtWZalDRs26Msvv9SgQYNSTABzNF9//bU8PDz00ksvpdg+Z84cxcfHq3379oaSZb5Ro0bp1VdflZubm0aNGnXH1zrqpOUbC/w1bdpUTzzxhGw2m7Zs2aJFixZp3Lhx6ty5s4YPH64tW7Zo1qxZpuNm2M2TGY8dO6b3339f/fr1S3OC+qOPPprd8ZBF/vWvf2nYsGHy8PBQmTJlZFmWjh8/br/txQN741OzXShniI+Pt958802rfPnypqNkmdKlS1tTpkxJtT0qKsoqXbq0gUTZp3z58tbKlStTbV+9erXD/ZqXLl3aOnfunP2/b/fw9/c3nDRrrV+/3vrHP/5hVatWzapatar1j3/8w9qwYYPpWJnKZrNZTk5O9psB3vq4sc/Jycl0VGSSqKgoy83NzRo9erR19epV+/arV69aI0eOtNzc3NL8c/5BwMjILW792p9lWbp06ZLy5cunb775Rs2bNzeYLuu4ublp3759qa4pHzlyRFWqVHG4Wec3c3Nz06FDh1S6dOkU20+ePKmAgAD9/fffZoIB9yE9a+T4+fllYZLsdbuvbqfl1pvJ5XRPPPGE/vnPf952JDsyMlIzZ87Uli1bsjnZ3fFtmlt8/vnnKX4jOzk5qVixYnryySdVqFAhg8myVtmyZTV79my9++67KbbPmjVL5cqVM5Qqe3h7e2vPnj2pysju3btVpEgRM6Gy2LVr11ShQgX997//VaVKlUzHyXa5Yc2NmwvG2rVrVatWLeXJk/KP/MTERG3cuNGhysjNX0k/f/68Bg0apMaNG9vvwbNp0yYtWbJEH3zwgaGEWWf//v1q0aLFbfeHhoY+uOdteGQGD4hvv/3WcnZ2tho3bmx99NFH1scff2w1btzYypMnjzVv3jzT8bJUv379LD8/P2vlypVWYmKilZiYaK1YscLy8/Oz+vTpYzpelnnooYesAwcOmI6R7TZt2mT5+/uneQnDUS9ZODk5Wb/99luq7efOnXPYc7Ysy2rZsqU1evToVNtHjx5ttWjRIvsDZTFPT0/r4MGDt91/6NAhy9PTMxsT3Tsn02XoQbN48WKtX7/e/vzLL79U1apV1aZNG/3xxx8Gk2WtVq1a6aefflLRokX13Xffad68eSpatKi2bNmS5t0fHcmgQYP05JNP6tlnn5W7u7vc3d0VHBysZ555Rp9++qnpeFnmjTfe0JAhQ5SYmGg6Sra6sebGvn37dOHCBf3xxx/2h6MN299g3eYO1OfPn091/y1HsmTJEoWEhKTa3rhxYy1fvtxAoqwVFBSk6dOn33b/tGnTVL169WxMdO+YM3KLKlWqaMiQIWratKn27t2rGjVqqE+fPlq5cqUCAgL09ddfm46ITGRZlqKjo1WsWDH9+uuv2rVrl9zd3VWlShWHGrpOywsvvKAVK1bIw8NDVapUSfWX0rx58wwly1q5ac2Nli1bSrq+flJISEiK2xskJSVpz549qlChghYvXmwqYpby8/NTjx491K9fvxTbhw4dqi+++MLh7j313//+V6Ghoerdu7f69OkjHx8fSdfXURo+fLhGjBih+fPn6/nnnzecNDXmjNzixIkT9mvoc+fOVbNmzfTpp59qx44datq0qeF0mSut+5LcjpeXVxYmMceyLJUrV0779+9XuXLlHH5+zM0KFiyoVq1amY6R7Z588kkdPXo0V5SRAgUKSLr++9zT01Pu7u72fXnz5tVTTz3lMKvMpuXGQpWrV6+2zxnZvHmzFi9erIkTJxpOl/mqVq2qzz//XH379tXw4cPtv/4XL16Us7Ozhg4d+kAWEYkykkrevHkVHx8vSVq+fLnatWsnSSpcuHC6/vLOCQoWLHjXWefWA3wvg8zg5OSkcuXK6fz587mqiCQmJqp+/fpq3LixihcvbjpOtnrjjTfUp08fnT171uHX3Lgxklu6dGn17dvXoS/JpKVDhw4KCAjQqFGjNG/ePFmWpUqVKmnDhg168sknTcfLdFWqVNGoUaN07NgxzZkzR0eOHJEklS9fXq1atXqgl8DnMs0tmjdvrqtXr6p27dr6+OOPdeLECZUsWVJLly5Vjx49dPjwYdMRM82aNWvu6XU7d+5URERE1oYxaOHChfr3v/+tsWPHKjAw0HScbJMvXz4dPHjQ4S9H3crJKfVUuQf9JmKZITExUatXr9axY8fUpk0beXp66syZM/Ly8pKHh4fpeMgEY8aM0dtvv61GjRpp/PjxKlq0qOlI94wycovo6GiFh4fr9OnT6tmzp/1eNL169VJSUtJdV610FBcvXtT06dM1ceJE7d6922H/gJaur0sQHx+vxMRE5c2bN8VQtuR4axHc0KBBA7355psKDQ01HSVb3W2egCOWs1OnTikkJETR0dFKSEjQ4cOHVaZMGUVEROjKlSsaN26c6YiZJi4uzn5Z+W6j2Y54+fnEiRPq3LmzDhw4oK+++irHrI1FGUEKK1eu1OTJkzVv3jz5+fmpVatWatWqlapVq2Y6WpaZMmXKHfc70nLwN5szZ47efvtt9erVS0FBQamG8B3pckVuFxoaKk9PT02aNElFihTR7t27VaZMGa1Zs0ZdunSxD+c7AmdnZ8XExMjb2/u2d2F39FEwSfriiy/Uq1cvBQQEpFpfZseOHYZS3R5zRtKQlJSk+fPn6+DBg7LZbKpYsaJCQ0NT/YI6il9++UVRUVGaPHmyLl++rNatW+vatWuaO3durlgQy1HLxt2EhYVJSnkPGke9XLFgwQI1adJELi4uWrBgwR1fm1P+JZke69ev14YNG5Q3b94U2/38/PTrr78aSpU1Vq5cqbi4OHl7e2vVqlWm4xhx6tQpzZ07V4ULF1aLFi1yxN9dD37CbLZv3z41b95cv/32mypUqCBJOnz4sIoVK6YFCxaoSpUqhhNmrqZNm2r9+vV6/vnnNXr0aIWEhMjZ2dmhhm3vRVJSkr777jt7Aa1UqZKaN28uZ2dn09GyzIkTJ0xHyDahoaE6e/asvL2973hZytFK2A3Jyclpntcvv/wiT09PA4myztNPPy0nJyeVLFlSDRo0sD9uXWHZUU2YMEF9+vRRw4YNtW/fPhUrVsx0pHvCZZpbPPXUU/L29taUKVPsy7//8ccf6tChg2JjY7Vp0ybDCTNXnjx51LNnT73++uspvk3i4uKi3bt354qRkaNHj6pp06b69ddfVaFCBVmWpcOHD8vX11cLFy7UI488YjoicF/CwsJUoEABffXVV/L09NSePXtUrFgxtWjRQqVKlXK49ZPWrVunNWvWaPXq1dq0aZOuXLmiUqVK6ZlnnrGXk5IlS5qOmelCQkK0ZcsWjRgxwv5N0JyCMnILd3d3bdu2TZUrV06xfd++fXr88ccd7qZpmzZt0uTJkzV79mxVrFhRbdu2VVhYmB566KFcU0aaNm0qy7I0ffp0FS5cWNL1lSlfeeUVOTk5aeHChYYTZq0DBw4oOjpaV69eTbHd0S5X/PTTT7pw4YKaNGli3zZ16lT1799fly9fVmhoqEaPHp1iYTBHcebMGTVo0EDOzs46cuSIatSooSNHjqho0aJau3atvL29TUfMMteuXdOmTZu0evVqrV69Wps3b1ZCQoLKli2rn3/+2XS8TNWoUSN9/fXXevjhh01HSb9sXHo+R3jsscesFStWpNq+YsUKKzAw0ECi7HH58mVr0qRJVu3atS0XFxfLycnJGjFihBUXF2c6WpbLly+ftWfPnlTbd+3aZeXPn99Aouxx7Ngx69FHH011q3knJyeHvF9JSEiI9e9//9v+fM+ePVaePHmsLl26WMOHD7eKFy9u9e/f31zALBYfH29NmjTJ6t69u/X6669bEyZMsOLj403Hyjbx8fHW0qVLrT59+lheXl4O+Xs8J6OMWJZ18eJF+2PhwoVW5cqVrTlz5linT5+2Tp8+bc2ZM8eqUqWKtXDhQtNRs8WhQ4esfv36WcWLF7fc3NysZs2amY6UpQoVKmRt2LAh1fb169dbhQoVMpAoezz//PNWixYtrNjYWMvDw8M6cOCAtW7dOuuJJ56w1q5dazpepitevLi1detW+/N3333Xql27tv357NmzrYCAABPRkAX+/vtva8WKFdb7779v1alTx3J1dbUqVqxovfbaa9b06dOtX375xXRE3ITLNFKqr3/d+JHc2Hbzc0ec3HY7SUlJ+uGHHzR58uS7fgMhJ2vXrp127NihSZMm6YknnpB0fUi/a9euCgoKUlRUlNmAWaRo0aJauXKlHn30URUoUEBbtmxRhQoVtHLlSvXp00c7d+40HTFTubm56ciRI/ZVKOvUqaOQkBC9//77kqSTJ0+qSpUqunTpksmYWeJ2///abDa5ubmpbNmy8vf3z+ZUWefpp5/W1q1b9cgjj6hevXp6+umn9fTTT9vv1YIHD9+mke7561+O9ofz3Tg7Oys0NNThF8UaNWqU2rdvr5o1a9qXBk9MTFTz5s01cuRIw+myTlJSkn3lzaJFi+rMmTOqUKGC/Pz8HO5auiT5+PjoxIkT8vX11dWrV7Vjxw4NHDjQvv/SpUuploZ3FKGhofavbd/s5q9y16lTR99995194n5OtnHjRpUoUUINGjRQ/fr1Va9evRy1GmmuZHJYJif4888/rS+//NKqVq0a1xgd3OHDh60FCxZY33//vXXkyBHTcbJcnTp1rPnz51uWZVn//Oc/rZCQEGv9+vVWu3btrMqVK5sNlwVeffVVq2bNmtbatWut3r17W0WKFLESEhLs+7/55hurRo0aBhNmneXLl1tPPvmktXz5cisuLs6Ki4uzli9fbj311FPWwoULrfXr11uVK1e2OnXqZDpqpvjrr7+sH3/80XrrrbesJ554wsqbN68VGBhode/e3ZozZ44VGxtrOiJuQRm5jRUrVlgvv/yy5e7ublWsWNF67733rB07dpiOhSywevVq0xGMWLx4sTV37lzLsq5PZg0ICLBsNptVtGhRa/ny5YbTZb7Y2FirTp06ls1mszw9Pa158+al2P/MM89Y7777rqF0Waty5cq3nRdVqVIly7Isa9myZZavr292R8sWcXFx1qJFi6x+/fpZjz/+uJU3b16HLNw5GZdpbpLbVyLNrRo1aqTixYurTZs2euWVV3LNzfIaN25s/+8yZcrowIEDunDhggoVKnTXuznnRMWKFdO6det08eJFeXh4pFrQbs6cOQ57w7hjx46leR8WLy8vHT9+XJJUrlw5nTt3LrujZYv8+fOrcOHCKly4sAoVKqQ8efLo4MGDpmPhJkxg/Z+bVyJ9+eWX7SuR5qbFv3Krc+fOaebMmZoxY4Y2bdqkwMBAvfLKK2rTpk3O/L7+XXTq1OmeXjd58uQsToLsUqdOHXl6emrq1Kn2FTl///13tWvXTpcvX9batWu1fPlyhYeHO8SdyZOTk7Vt2zatXr1aq1at0oYNG3T58uVUq7I64k0RcyrKyP+wEimk60uk/+c//9GMGTN06NAh1atXTytXrjQdK1M5OTnJz89P1apVSzWh8Wbz58/PxlTISj///LNatGhhn8Brs9kUHR2tMmXK6Pvvv1f58uX13Xff6dKlS2rbtq3puPfNy8tLly9fVokSJVS/fn3Vr19fDRo0YDXlBxhl5H9YiRQ3JCUl6ccff9QHH3ygPXv2ONzXucPDwzVz5kyVKlVKnTp10iuvvGJfeRaOy7IsLVmyRIcPH5ZlWapYsaIaNWokJycn09Ey3fjx49WgQQOVL1/edBTcI8rILeLj4zVz5kxNnjxZW7ZsUVJSkiIjI9WpUyeHu6EUUtqwYYOmT5+ub7/9VleuXFHz5s318ssvp1g+3FEkJCRo3rx5mjx5sjZu3KjnnntOnTt3VnBwsEPOFwHwYKOM3MHPP/+sSZMmadq0afrzzz/VqFEjh178K7d69913NWPGDJ05c0YNGzbUyy+/rNDQUOXLl890tGxx6tQpRUVFaerUqbp27ZoOHDjgsBM5c7M1a9Zo2LBh9jtTBwQEqF+/fqpbt67paIAcb3wuE1WoUEGfffaZfvnlF82YMcN0HGSR1atXq2/fvvr111+1cOFCtWnTJtcUEen6wlc3Fr9KTk42HQdZ4JtvvlHDhg2VL18+9ezZUz169JC7u7ueffZZ/ec//zEdD2BkBMiNbr5Mc+NbZB07dlRISIhDziHI7QICAvTqq6+qV69eKbZHRkZqwoQJfM0VxlFGkCstWLBATZo0kYuLy10vvTVv3jybUmWPmyewduzYUa+88oqKFCliOhaykKurq/bv36+yZcum2H706FEFBgbqypUrhpIB11FGkCs5OTnp7Nmz8vb2vuNIgCPeHNHJyUmlSpVStWrV7jhZdd68edmYClmpbNmy6tevn1577bUU28ePH69hw4bpyJEjhpIB17ECK3Klm+dG5LZ5Eu3ateMbM7lEp06dNHLkSPXp00c9e/bUrl27VKtWLdlsNq1fv15RUVEOfTNI5ByMjCBXS05OVlRUlObNm6eTJ0/KZrOpTJkyatWqldq2bctf2sjRnJ2dFRMTI29vb82fP1/Dhw+3zw+58W2aFi1aGE4JUEaQi1mWpWbNmmnRokV67LHHVLFiRVmWpYMHD2rv3r1q3ry5vvvuO9MxgQy7+XIk8CDjMg1yraioKK1du1YrVqxQgwYNUuxbuXKlQkNDNXXqVLVr185QQuD+MbqHnICREeRawcHBeuaZZ/T222+nuf/TTz/VmjVrtGTJkmxOBmQOJycnFShQ4K6F5MKFC9mUCEgbIyPItfbs2aPPPvvstvubNGmiUaNGZWMiIPMNHDhQBQoUMB0DuCNGRpBr5c2bV6dOnVKJEiXS3H/mzBn5+/srISEhm5MBmYM5I8gpWGoRuVZSUpLy5Ln94KCzs7MSExOzMRGQuZgvgpyCyzTItSzLUocOHeTq6prmfkZEkNMx8I2cgjKCXKt9+/Z3fQ3fpEFOltsW9EPOxZwRAABgFHNGAACAUZQRAABgFGUEAAAYRRkBAABGUUYA3FVUVJQKFiz4wLwPAMdCGQFyuA4dOshms6lbt26p9oWHh8tms6lDhw739RlhYWE6fPiw/fmAAQNUtWrV+3rPu0lKStLgwYNVsWJFubu7q3Dhwnrqqaf09ddf219Tv359RUREpPu9O3TooNDQ0MwLC+C+sM4I4AB8fX01c+ZMff7553J3d5ckXblyRTNmzFCpUqXu672vXbsmd3d3+/tmlwEDBuirr77SF198oRo1aiguLk7btm3TH3/8ka05AGQ9RkYAB1C9enWVKlVK8+bNs2+bN2+efH19Va1aNfu2xYsXq06dOipYsKCKFCmi559/XseOHbPvP3nypGw2m2bPnq369evLzc1N33zzTYrLK1FRURo4cKB2794tm80mm82mqKgoSVJkZKSqVKmi/Pnzy9fXV+Hh4frrr78ydE4//PCDwsPD9dJLL8nf31+PPfaYOnfurN69e0u6PrqxZs0ajRw50p7j5MmTSkpKUufOneXv7y93d3dVqFBBI0eOtL/vgAEDNGXKFH3//ff241avXq3Vq1fLZrPpzz//tL92165d9veVpFOnTqlZs2YqVKiQ8ufPr8qVK2vRokUZOj8A/48yAjiIjh07priEMXnyZHXq1CnFay5fvqzevXtr69atWrFihZycnPTCCy+kWqnzrbfeUs+ePXXw4EE1btw4xb6wsDD16dNHlStXVkxMjGJiYhQWFibp+o3ZRo0apX379mnKlClauXKl/vWvf2XofIoXL66VK1fq999/T3P/yJEjVbNmTXXt2tWew9fXV8nJyXr44Yc1e/ZsHThwQB9++KHeffddzZ49W5LUt29ftW7dWiEhIfbjatWqdU+ZunfvroSEBK1du1Z79+7VkCFD5OHhkaHzA/D/uEwDOIi2bdvqnXfesY9ubNiwQTNnztTq1avtr2nVqlWKYyZNmiRvb28dOHBAgYGB9u0RERFq2bJlmp/j7u4uDw8P5cmTR8WLF0+x7+b5G/7+/vr444/1+uuva8yYMek+n8jISL344osqXry4KleurFq1aqlFixZq0qSJJKlAgQLKmzev8uXLlyKHs7OzBg4cmCLHxo0bNXv2bLVu3VoeHh5yd3dXQkJCqvx3Ex0drVatWqlKlSqSpDJlyqT7vACkxsgI4CCKFi2q5557TlOmTNHXX3+t5557TkWLFk3xmmPHjqlNmzYqU6aMvLy85O/vL+n6X7I3q1GjRoYyrFq1So0aNVLJkiXl6empdu3a6fz587p8+XK636tSpUrat2+fNm/erI4dO+q3335Ts2bN1KVLl7seO27cONWoUUPFihWTh4eHJkyYkOocM6Jnz54aNGiQateurf79+2vPnj33/Z4AKCOAQ+nUqZOioqI0ZcqUVJdoJKlZs2Y6f/68JkyYoJ9++kk//fSTJOnq1aspXpc/f/50f/apU6fUtGlTBQYGau7cudq+fbu+/PJLSdcnwWaEk5OTHn/8cfXq1Uvz589XVFSUJk2apBMnTtz2mNmzZ6tXr17q1KmTli5dql27dqljx46pzjGtz5JS3un21txdunTR8ePH1bZtW+3du1c1atTQ6NGjM3RuAP4fZQRwICEhIbp69aquXr2aaq7H+fPndfDgQb3//vt69tlnFRAQkOFvpuTNm1dJSUkptm3btk2JiYkaPny4nnrqKZUvX15nzpzJ8LmkpVKlSpJkH2lJK8e6detUq1YthYeHq1q1aipbtmyKSbq3O65YsWKSpJiYGPu2Xbt2pcrg6+urbt26ad68eerTp48mTJhw3+cF5HbMGQEciLOzsw4ePGj/75sVKlRIRYoU0VdffaUSJUooOjpab7/9doY+p3Tp0jpx4oR27dqlhx9+WJ6ennrkkUeUmJio0aNHq1mzZtqwYYPGjRuX4XN58cUXVbt2bdWqVUvFixfXiRMn9M4776h8+fKqWLGiPcdPP/2kkydPysPDQ4ULF1bZsmU1depULVmyRP7+/po2bZq2bt1qvyR147glS5bo559/VpEiRVSgQAGVLVtWvr6+GjBggAYNGqQjR45o+PDhKTJFRESoSZMmKl++vP744w+tXLlSAQEBGT5HANcxMgI4GC8vL3l5eaXa7uTkpJkzZ2r79u0KDAxUr169NHTo0Ax9RqtWrRQSEqIGDRqoWLFimjFjhqpWrarIyEgNGTJEgYGBmj59ugYPHpzh82jcuLF++OEHNWvWTOXLl1f79u1VsWJFLV26VHnyXP93VN++feXs7KxKlSqpWLFiio6OVrdu3dSyZUuFhYXpySef1Pnz5xUeHp7ivbt27aoKFSrY55Vs2LBBLi4umjFjhg4dOqTHHntMQ4YM0aBBg1Icl5SUpO7duysgIEAhISGqUKFChibnAkjJZt18gRQAACCbMTICAACMoowAMKJy5cry8PBI8zF9+nTT8QBkIy7TADDi1KlTt/3Kr4+Pjzw9PbM5EQBTKCMAAMAoLtMAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMOr/AEMs7Qgx4XQgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tab = pd.crosstab(df['Marital_Status'], df['Response'], normalize='index')\n",
    "\n",
    "tab.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 447
    },
    "id": "fsLjLXE8oGgp",
    "outputId": "e79d2d31-6c99-4cf9-a798-c4c8e4006c84"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Marital_Status'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAAATEb4AAABMxN2OAADA6fyZekk/Jxy3fk5I+lVx+w+p+LUSCggIcGBl99jIV0xMjCwWiy5cuHDTbaKjo1W0aFHTagIAALlv148HVa/tY6rX9jFJ0ugps1Sv7WOaNGmSgysrwCNf8+fP17hx43T+/Hm5uV3/Gn/++aeKFSum+++/X1u3brVuu3XrVjVv3lyHDh3SqVOn5Odn4lN5AQBwRk/EOLqCW2rRuIGMX3/IuoLnfNmvZcuW+vPPP7Vr1y5r29atW1W2bFl9//33unTpkrU9JiZG5cuXV3BwsMqWLSuLJZ8/TA4AADitAhu+QkJCVL58ecXExFjbYmJi1LlzZ1WpUkXbt2+3aW/ZsmW2lx2jo6MVEBAgb29vPfLIIzp79myWc82bN09VqlSRh4eHQkJC9MEHH1jXjRkzRh07drR+fvPNN2WxWPS///3PptYFCxbk0jcHAAAFWYENX5LUokULbdq0yfp506ZNatGihSIiIqzt6enp+vbbb9WyZcss+3/33XcaOHCghg0bpri4OLVs2VKvvPKKzTZr1qxRZGSkxowZo/379+vJJ5/UgAEDrMdv0aKFtm7dqszMTEnS5s2bVbJkSW3evFmSdPr0aR0+fFgRERHZfoe0tDSlpKTYLAAAwHkV+PC1bds2Xbt2TRcvXtSePXvUvHlzRUREWEfEduzYocuXL2cbvmbPnq22bdvq+eefV3BwsEaMGKG2bdvabPPGG2+of//+GjZsmIKDgzV69Gh17dpVb7zxhiSpefPm1nMbhqGtW7dqzJgx1vNv2rRJZcqUUfXq1bP9DtOmTZOfn5918ff3z70OAgAA+U6BDl8tW7ZUamqqvv/+e23dulXBwcEqXbq0IiIi9P333ys1NVUxMTEKCAhQUFBQlv3j4+PVqFEjm7Z/fo6Pj1eTJk1s2po0aaL4+HhJkp+fn+rWrauYmBjt27dPLi4uevLJJ/Xjjz/q4sWLiomJuemolySNHz9eycnJ1uX48eM33RYAABR8BfZuR0mqWrWqKlasqE2bNun8+fPWkFO2bFkFBgZq27Zt2rRpkx544IFs9zcM447O888J+oZh2LS1aNFCMTEx8vDwUEREhIoVK6ZatWpp27ZtiomJ0ciRI296bE9PT3l6et5RHQAA5Jr//xt4hz+F0J3nhtsp0CNfkqwT6WNiYtSiRQtre0REhL788kvt2LEj20uOklSzZk3t2LHDpu2fn2vUqKHY2Fibtu3bt6tGjRrWzzfmfX3zzTfWGiIiIrRixYpbzvcCAMBR3NPOSRnpunTV0ZUUHOnp6ZIkV1fXuzpOgR75kq6Hr6efflpXr161CTkRERF66qmndOXKlZuGrxEjRqhx48aaPn26unTpog0bNmj9+vU224wbN07du3dXeHi4HnzwQX322WdavXq1vvrqK+s2N+Z9ffbZZ9YJ+y1atFC3bt1UqlQp1axZMw++OQAA9nO9dklFf/lCZzwelVRU3u7SPfEkpitX7NotMzNTv//+u7y9va3PF7WXU4Svy5cvq3r16ipTpoy1PSIiQhcvXlSVKlVuOon9/vvv1/vvv6/JkycrKipKrVq10osvvqiXX37Zuk2XLl00e/ZszZgxQyNGjFBgYKAWL15sM8rm5+enevXqKSkpyRq0mjVrpszMTEa9AAD5VtkjH0qSzlRqL7l6OLgak6Qm2L2ri4uLAgIC7vp5oRYjty5gIlekpKRcv+tx5Cq5eHo7uhwAwD8kevVydAm5LsO1kK56lbg3hr6G77r9Njfh4eEhF5fsZ2zd+P1OTk6Wr6/vLY9T4Ee+AADA3XHNuCzX1BOOLsMcXl6OrqDgT7gHAAAoSAhfAAAAJiJ8AQAAmIjwBQAAYCIm3OdT+6e0ve3dEgAAR0h2dAEo4Bj5AgAAMBHhCwAAwESELwAAABMRvgAAAExE+AIAADAR4QsAAMBEhC8AAAATEb4AAABMRPgCAAAwEeELAADARIQvAAAAExG+AAAATET4AgAAMBHhCwAAwESELwAAABMRvgAAAExE+AIAADAR4QsAAMBEhC8AAAATEb4AAABMRPgCAAAwEeELAADARG6OLgDZC538pVw8vR1dBgAAVolevRxdwt2LSnZ0BYx8AQAAmInwBQAAYCLCFwAAgIkIXwAAACYifAEAAJiI8JWNxMREWSwWxcXFOboUAADgZBwaviwWyy2X/v3753kN/fv3V5cuXfL8PAAAAJKDn/N16tQp698rV67UpEmTdOjQIWtboUKFHFFWnklPT5eHh4ejywAAAA7k0JGvsmXLWhc/Pz9ZLBabtg8//FBVqlSRh4eHQkJC9MEHH9js/9NPP6lp06by8vJSzZo19dVXX8lisWjt2rXWbX799Vf16NFDxYoVU4kSJdS5c2clJiZKkqKiorRkyRJ98skn1tG2mJgY677Hjh1Ty5Yt5e3trbCwMH377bc259++fbuaN2+uQoUKyd/fXyNGjFBqaqp1feXKlfXKK6+of//+8vPz05AhQ3K9DwEAQMGSb+d8rVmzRpGRkRozZoz279+vJ598UgMGDNCmTZskSZmZmerSpYu8vb313Xff6d1339WECRNsjnHp0iW1bNlSPj4+2rJli2JjY+Xj46N27dopPT1dY8eOVffu3dWuXTudOnVKp06dUuPGja37T5gwQWPHjlVcXJyCg4P12GOP6dq1a5Kkffv2qW3bturatav27t2rlStXKjY2VsOHD7epYcaMGQoNDdXu3bs1ceLELN8zLS1NKSkpNgsAAHBe+fb1Qm+88Yb69++vYcOGSZJGjx6tHTt26I033lDLli21YcMGHT16VDExMSpbtqwk6dVXX1Xr1q2tx1ixYoVcXFz0/vvvy2KxSJIWL16sokWLKiYmRm3atFGhQoWUlpZmPcbfjR07Vg8//LAkacqUKapVq5Z+/vlnVa9eXTNmzFCvXr00cuRISVK1atU0Z84cRUREaN68efLy8pIkPfDAAxo7duxNv+e0adM0ZcqUu+8wAABQIOTbka/4+Hg1adLEpq1JkyaKj4+XJB06dEj+/v42oem+++6z2X737t36+eefVaRIEfn4+MjHx0fFixfXlStXdPTo0dvWUKdOHevf5cqVkySdOXPGeuzo6GjrcX18fNS2bVtlZmYqISHBul+DBg1ueY7x48crOTnZuhw/fvy2dQEAgIIr3458SbKOVt1gGIa17e9/30xmZqbq16+vZcuWZVlXqlSp257f3d09Sy2ZmZnW//vkk09qxIgRWfYLCAiw/l24cOFbnsPT01Oenp63rQUAADiHfBu+atSoodjYWPXt29fatn37dtWoUUOSVL16dSUlJem3335TmTJlJEnff/+9zTHCw8O1cuVKlS5dWr6+vtmex8PDQxkZGTmuLzw8XAcOHFDVqlVzvC8AALh35dvLjuPGjVN0dLTmz5+vI0eOaNasWVq9erV1/lTr1q1VpUoV9evXT3v37tW2bdusE+5vjFL17t1bJUuWVOfOnbV161YlJCRo8+bNioyM1IkTJyRdvyNx7969OnTokP744w9dvXr1jup77rnn9O233+rpp59WXFycjhw5ok8//VTPPPNMHvQGAABwFvk2fHXp0kWzZ8/WjBkzVKtWLS1YsECLFy9WixYtJEmurq5au3at/vzzT/3rX//S4MGD9eKLL0qSdbK7t7e3tmzZooCAAHXt2lU1atTQwIEDdfnyZetI2JAhQxQSEqIGDRqoVKlS2rZt2x3VV6dOHW3evFlHjhxRs2bNVK9ePU2cONE6NwwAACA7FsMwDEcXkVu2bdumpk2b6ueff1aVKlUcXY5dUlJS5OfnJ/+Rq+Ti6e3ocgAAsEr06uXoEu5eVHKeHPbG73dycvJNpzrdkG/nfN2JNWvWyMfHR9WqVdPPP/+syMhINWnSpMAGLwAA4PwKdPi6ePGinn32WR0/flwlS5ZUq1atNHPmTEeXBQAAcFMFOnz17dvX5m5IAACA/C7fTrgHAABwRoQvAAAAExXoy47ObP+Utre9WwIAAHPlzZ2C9xpGvgAAAExE+AIAADAR4QsAAMBEhC8AAAATEb4AAABMRPgCAAAwEeELAADARIQvAAAAExG+AAAATET4AgAAMBHhCwAAwER2h6/169crNjbW+vmdd95R3bp11atXL50/fz5XigMAAHA2doevcePGKSUlRZK0b98+jRkzRg899JCOHTum0aNH51qBAAAAzsTN3h0TEhJUs2ZNSdLHH3+sDh06aOrUqfrhhx/00EMP5VqBAAAAzsTukS8PDw9dunRJkvTVV1+pTZs2kqTixYtbR8QAAABgy+6Rr6ZNm2r06NFq0qSJdu7cqZUrV0qSDh8+rIoVK+ZagQAAAM7E7pGvt99+W25ubvroo480b948VahQQZL0xRdfqF27drlWIAAAgDOxGIZhOLoI/CUlJUV+fn5KTk6Wr6+vo8sBAAB3ICe/33ZfdkxKSrrl+oCAAHsPDQAA4LTsDl+VK1eWxWK56fqMjAx7Dw0AAOC07A5fe/bssfl89epV7dmzR7NmzdKrr75614UBAAA4I7vDV1hYWJa2Bg0aqHz58poxY4a6du16V4UBAAA4I7vD180EBwfr+++/z+3D3nNCJ38pF09vR5cBALgDiV69HF0C7lRUsqMrsD98/fNBqoZh6NSpU4qKilK1atXuujAAAABnZHf4Klq0aJYJ94ZhyN/fXytWrLjrwgAAAJyR3eFr06ZNNp9dXFxUqlQpVa1aVW5uuX41EwAAwCnYnZIsFosaN26cJWhdu3ZNW7ZsUfPmze+6OAAAAGdj9+uFWrZsqXPnzmVpT05OVsuWLe+qKAAAAGdld/gyDCPbh6yePXtWhQsXvqui8orFYtHatWtz9ZhRUVGqW7durh4TAAA4rxxfdrzx/C6LxaL+/fvL09PTui4jI0N79+5V48aNc6/CHDhz5owmTpyoL774Qr/99puKFSumsLAwRUVFqVGjRjp16pSKFSvmkNoAAAAkO8KXn5+fpOsjX0WKFFGhQoWs6zw8PHT//fdryJAhuVdhDnTr1k1Xr17VkiVLFBQUpN9++01ff/219fJo2bJlHVIXAADADTkOX4sXL5Z0/d2OY8eOzTeXGC9cuKDY2FjFxMQoIiJCklSpUiXdd9991m0sFovWrFmjLl26KDExUYGBgfr444/11ltv6bvvvlO1atU0f/58NWrUyLrPe++9p5deeklnz55V27Zt1axZM7300ku6cOHCTWtZvHixpk+froSEBFWuXFkjRozQsGHD8uy7AwCAgsPuOV+TJ0/ON8FLknx8fOTj46O1a9cqLS3tjvebMGGCxo4dq7i4OAUHB+uxxx7TtWvXJEnbtm3T0KFDFRkZqbi4OLVu3fq276187733NGHCBL366quKj4/X1KlTNXHiRC1ZsiTb7dPS0pSSkmKzAAAA53VXD+T66KOPtGrVKiUlJSk9Pd1m3Q8//HBXheWUm5uboqOjNWTIEM2fP1/h4eGKiIhQz549VadOnZvuN3bsWD388MOSpClTpqhWrVr6+eefVb16db311ltq3769xo4dK+n6q5O2b9+uzz///KbHe/nllzVz5kzr3LjAwEAdPHhQCxYsUL9+/bJsP23aNE2ZMuVuvjoAAChA7B75mjNnjgYMGKDSpUtrz549uu+++1SiRAkdO3ZM7du3z80a71i3bt108uRJffrpp2rbtq1iYmIUHh6u6Ojom+7z92BWrlw5Sdcn7kvSoUOHbC5bSsry+e9+//13HT9+XIMGDbKOxPn4+OiVV17R0aNHs91n/PjxSk5Oti7Hjx+/068LAAAKILtHvubOnat3331Xjz32mJYsWaJnn31WQUFBmjRpUrbP/zKLl5eXWrdurdatW2vSpEkaPHiwJk+erP79+2e7vbu7u/XvG4/OyMzMlJT94zQMw7jpuW/s995776lhw4Y261xdXbPdx9PT0+aOUQAA4NzsHvlKSkqyPlKiUKFCunjxoiTp8ccf1/Lly3OnulxQs2ZNpaam2rVv9erVtXPnTpu2Xbt23XT7MmXKqEKFCjp27JiqVq1qswQGBtpVAwAAcC52j3yVLVtWZ8+eVaVKlVSpUiXt2LFDYWFhSkhIuOXoUF45e/as/v3vf2vgwIGqU6eOihQpol27dmn69Onq3LmzXcd85pln1Lx5c82aNUsdO3bUN998oy+++CLbh8veEBUVpREjRsjX11ft27dXWlqadu3apfPnz2v06NH2fj0AAOAk7B75euCBB/TZZ59JkgYNGqRRo0apdevW6tGjhx555JFcK/BO+fj4qGHDhvrPf/6j5s2bKzQ0VBMnTtSQIUP09ttv23XMJk2aaP78+Zo1a5bCwsK0fv16jRo1Sl5eXjfdZ/DgwXr//fcVHR2t2rVrKyIiQtHR0Yx8AQAASZLFsHOYKjMzU5mZmdYXa69atUqxsbGqWrWqhg4dKg8Pj1wtNL8YMmSIfvrpJ23dujVPjp+SkiI/Pz/5j1wlF0/vPDkHACB3JXr1cnQJuFNRyXly2Bu/38nJyfL19b3ltnZfdjxx4oT8/f2tn7t3767u3bvLMAwdP35cAQEB9h46X3njjTfUunVrFS5cWF988YWWLFmiuXPnOrosAABQQNkdvgIDA3Xq1CmVLl3apv3cuXMKDAxURkbGXReXH+zcuVPTp0/XxYsXFRQUpDlz5mjw4MGOLgsAABRQdoev7B7DIEl//vnnLedEFTSrVq1ydAkAAMCJ5Dh83bhjz2KxaOLEifL2/mteUkZGhr777jvVrVs31woEAABwJjkOX3v27JF0feRr3759NhPrPTw8FBYWZn0dDwAAAGzZfbfjgAEDNHv27NvO6EfO5ORuCQAAkD+Ycrfj4sWLbT7/8ssvSk1NVfXq1eXiYvfjwwAAAJxajlPSkiVL9Oabb9q0PfHEEwoKClLt2rUVGhrKy6EBAABuIsfha/78+fLz87N+Xr9+vRYvXqylS5fq+++/V9GiRTVlypRcLRIAAMBZ5Piy4+HDh9WgQQPr508++USdOnVS7969JUlTp07VgAEDcq9CAAAAJ5Ljka/Lly/bTCTbvn27mjdvbv0cFBSk06dP5051AAAATibH4atSpUravXu3JOmPP/7QgQMH1LRpU+v606dP21yWBAAAwF9yfNmxb9++evrpp3XgwAF98803ql69uurXr29dv337doWGhuZqkQAAAM4ix+Hrueee06VLl7R69WqVLVtW//3vf23Wb9u2TY899liuFQgAAOBM7H7I6p1avny5OnXqpMKFC+flaZwGD1kFAKDgycnvd54/DfXJJ5/Ub7/9ltenAQAAKBDyPHzl8cAaAABAgcJ7gAAAAExE+AIAADAR4QsAAMBEhC8AAAAT5Xn4qlSpktzd3fP6NAAAAAVCjh+ymlP79+/P61MAAAAUGDkKX8WKFZPFYrmjbc+dO2dXQQAAAM4sR+HrzTffzKMyAAAA7g05Cl/9+vXLqzoAAADuCbky5+vy5cu6evWqTRvvJQQAAMjK7rsdU1NTNXz4cJUuXVo+Pj4qVqyYzQIAAICs7B75evbZZ7Vp0ybNnTtXffv21TvvvKNff/1VCxYs0GuvvZabNd6TQid/KRdPb0eXAQC4ByV69XJ0CXknKtnRFdgfvj777DMtXbpULVq00MCBA9WsWTNVrVpVlSpV0rJly9S7d+/crBMAAMAp2H3Z8dy5cwoMDJR0fX7XjUdLNG3aVFu2bMmd6gAAAJyM3eErKChIiYmJkqSaNWtq1apVkq6PiBUtWjQ3agMAAHA6doevAQMG6Mcff5QkjR8/XnPnzpWnp6dGjRqlcePG5VqBAAAAzsTuOV+jRo2y/t2yZUv99NNP2rVrl6pUqaKwsLBcKQ4AAMDZ2D3ytXTpUqWlpVk/BwQEqGvXrqpRo4aWLl2aK8U5WuXKle/6qf5RUVGqW7durtQDAAAKvru67JicnPV2zYsXL2rAgAF3VdTf9e/fXxaLRUOHDs2ybtiwYbJYLOrfv3+une/vvv/+ez3xxBN5cmwAAHBvsjt8GYaR7Uu2T5w4IT8/v7sq6p/8/f21YsUKXb582dp25coVLV++XAEBAXd17H8+mV+S0tPTJUmlSpWStzfP2gIAALknx+GrXr16Cg8Pl8Vi0YMPPqjw8HDrEhYWpmbNmqlVq1a5WmR4eLgCAgK0evVqa9vq1avl7++vevXqWdvWr1+vpk2bqmjRoipRooQ6dOigo0ePWtcnJibKYrFo1apVatGihby8vPR///d/6t+/v7p06aJp06apfPnyCg4OlpT1smNycrKeeOIJlS5dWr6+vnrggQesNx3c8Nprr6lMmTIqUqSIBg0apCtXruRqXwAAgIItxxPuu3TpIkmKi4tT27Zt5ePjY13n4eGhypUrq1u3brlW4A0DBgzQ4sWLrQ9vXbRokQYOHKiYmBjrNqmpqRo9erRq166t1NRUTZo0SY888oji4uLk4vJXznzuuec0c+ZMLV68WJ6entq8ebO+/vpr+fr6auPGjTIMI8v5DcPQww8/rOLFi2vdunXy8/PTggUL9OCDD+rw4cMqXry4Vq1apcmTJ+udd95Rs2bN9MEHH2jOnDkKCgrK9f4AAAAFU47D1+TJkyVdHxXq0aOHvLy8cr2o7Dz++OMaP368dfRq27ZtWrFihU34+mfoW7hwoUqXLq2DBw8qNDTU2j5y5Eh17drVZtvChQvr/fffl4eHR7bn37Rpk/bt26czZ87I09NTkvTGG29o7dq1+uijj/TEE0/ozTff1MCBAzV48GBJ0iuvvKKvvvrqlqNfaWlpNjcupKSk3FmHAACAAsnuOV/9+vUzLXhJUsmSJfXwww9ryZIlWrx4sR5++GGVLFnSZpujR4+qV69eCgoKkq+vr/UJ/ElJSTbbNWjQIMvxa9eufdPgJUm7d+/Wn3/+qRIlSsjHx8e6JCQkWC9txsfHq1GjRjb7/fPzP02bNk1+fn7Wxd/f/5bbAwCAgi1HI1/FixfX4cOHVbJkSRUrVizbCfc33HjdUG4aOHCghg8fLkl65513sqzv2LGj/P399d5776l8+fLKzMxUaGiodQL9DYULF86yb3Ztf5eZmaly5crZjLTdcDdP9B8/frxGjx5t/ZySkkIAAwDAieUofP3nP/9RkSJFJOmun39lj3bt2lmDVNu2bW3WnT17VvHx8VqwYIGaNWsmSYqNjc21c4eHh+v06dNyc3NT5cqVs92mRo0a2rFjh/r27Wtt27Fjxy2P6+npab2MCQAAnF+Owle/fv0kSdeuXZN0PQCVLVs296u6CVdXV8XHx1v//rtixYqpRIkSevfdd1WuXDklJSXp+eefz7Vzt2rVSo0aNVKXLl30+uuvKyQkRCdPntS6devUpUsXNWjQQJGRkerXr58aNGigpk2batmyZTpw4AAT7gEAgJVdc77c3Nz01FNP2UwUN4uvr698fX2ztLu4uGjFihXavXu3QkNDNWrUKM2YMSPXzmuxWLRu3To1b95cAwcOVHBwsHr27KnExESVKVNGktSjRw9NmjRJzz33nOrXr69ffvlFTz31VK7VAAAACj6Lkd1zFe5Ay5YtFRkZaX30BHJHSkrK9Yn3I1fJxZMHvAIAzJfo1cvRJeSdqKxv58kNN36/k5OTsx0k+ju7X6w9bNgwjRkzRidOnFD9+vWzTFivU6eOvYcGAABwWnaHrx49ekiSRowYYW2zWCzW1w5lZGTcfXUAAABOxu7wlZCQkJt1AAAA3BPsDl+VKlXKzToAAADuCXaHrxsOHjyopKSkLA8y7dSp090eGgAAwOnYHb6OHTumRx55RPv27bPO9ZJkfeo9c77uzv4pbW97twQAAHkjb+4IxHV2v9sxMjJSgYGB+u233+Tt7a0DBw5oy5YtatCgQbav4AEAAMBdjHx9++23+uabb1SqVCm5uLjIxcVFTZs21bRp0zRixAjt2bMnN+sEAABwCnaPfGVkZMjHx0eSVLJkSZ08eVLS9Yn4hw4dyp3qAAAAnIzdI1+hoaHau3evgoKC1LBhQ02fPl0eHh569913eZchAADATdgdvl588UWlpqZKkl555RV16NBBzZo1U4kSJbRixYpcKxAAAMCZ2P1ux+ycO3dOxYoVs97xiJzLybuhAABA/pCn73YcOHDgHW23aNGinB4aAADA6eU4fEVHR6tSpUqqV6+ecnHQDAAA4J6Q4/A1dOhQrVixQseOHdPAgQPVp08fFS9ePC9qAwAAcDo5ftTE3LlzderUKT333HP67LPP5O/vr+7du+vLL79kJAwAAOA27nrC/S+//KLo6GgtXbpUV69e1cGDB63P/0LOMeEeAICCJye/33Y/ZPUGi8VifbdjZmbm3R4OAADAqdkVvtLS0rR8+XK1bt1aISEh2rdvn95++20lJSUx6gUAAHALOZ5wP2zYMK1YsUIBAQEaMGCAVqxYoRIlSuRFbQAAAE4nx3O+XFxcFBAQoHr16t3yYaqrV6++6+LuRcz5AgCg4MnTh6z27duXJ9gDAADYya6HrAIAAMA+d323IwAAAO4c4QsAAMBEhC8AAAATEb4AAABMRPgCAAAwUY7vdoQ5Qid/KRdPb0eXAQDIRYlevRxdAqKSHV0BI18AAABmInwBAACYiPAFAABgIsIXAACAiQhfAAAAJiJ8AQAAmKhAhC+LxaK1a9c6ugy7tGjRQiNHjnR0GQAAIJ9waPjq37+/LBaLLBaL3N3dVaZMGbVu3VqLFi1SZmamdbtTp06pffv2DqwUAAAgdzh85Ktdu3Y6deqUEhMT9cUXX6hly5aKjIxUhw4ddO3aNUlS2bJl5enpmWc1ZGRk2IQ9AACAvOLw8OXp6amyZcuqQoUKCg8P1wsvvKBPPvlEX3zxhaKjoyXZXnZs1KiRnn/+eZtj/P7773J3d9emTZskSefPn1ffvn1VrFgxeXt7q3379jpy5Ih1++joaBUtWlSff/65atasKU9PT/3yyy9KS0vTs88+K39/f3l6eqpatWpauHChdb+DBw/qoYceko+Pj8qUKaPHH39cf/zxh3V9amqq+vbtKx8fH5UrV04zZ87Mo14DAAAFlcPDV3YeeOABhYWFafXq1VnW9e7dW8uXL5dhGNa2lStXqkyZMoqIiJB0/XLmrl279Omnn+rbb7+VYRh66KGHdPXqVes+ly5d0rRp0/T+++/rwIEDKl26tPr27asVK1Zozpw5io+P1/z58+Xj4yPp+qXPiIgI1a1bV7t27dL69ev122+/qXv37tZjjhs3Tps2bdKaNWu0YcMGxcTEaPfu3bf8rmlpaUpJSbFZAACA88q373asXr269u7dm6W9R48eGjVqlGJjY9WsWTNJ0ocffqhevXrJxcVFR44c0aeffqpt27apcePGkqRly5bJ399fa9eu1b///W9J0tWrVzV37lyFhYVJkg4fPqxVq1Zp48aNatWqlSQpKCjIet558+YpPDxcU6dOtbYtWrRI/v7+Onz4sMqXL6+FCxdq6dKlat26tSRpyZIlqlix4i2/57Rp0zRlyhR7uwkAABQw+XLkS5IMw5DFYsnSXqpUKbVu3VrLli2TJCUkJOjbb79V7969JUnx8fFyc3NTw4YNrfuUKFFCISEhio+Pt7Z5eHioTp061s9xcXFydXW1jp790+7du7Vp0yb5+PhYl+rVq0uSjh49qqNHjyo9PV2NGjWy7lO8eHGFhITc8nuOHz9eycnJ1uX48eO36xoAAFCA5duRr/j4eAUGBma7rnfv3oqMjNRbb72lDz/8ULVq1bKOYP39cuTf/TPMFSpUKMvnW8nMzFTHjh31+uuvZ1lXrlw5mzllOeHp6ZmnNxMAAID8JV+OfH3zzTfat2+funXrlu36Ll266MqVK1q/fr0+/PBD9enTx7quZs2aunbtmr777jtr29mzZ3X48GHVqFHjpuesXbu2MjMztXnz5mzXh4eH68CBA6pcubKqVq1qsxQuXFhVq1aVu7u7duzYYd3n/PnzOnz4cE6/PgAAcGIOD19paWk6ffq0fv31V/3www+aOnWqOnfurA4dOqhv377Z7lO4cGF17txZEydOVHx8vHr16mVdV61aNXXu3FlDhgxRbGysfvzxR/Xp00cVKlRQ586db1pH5cqV1a9fPw0cOFBr165VQkKCYmJitGrVKknS008/rXPnzumxxx7Tzp07dezYMW3YsEEDBw5URkaGfHx8NGjQII0bN05ff/219u/fr/79+8vFxeFdDAAA8hGHJ4P169erXLlyqly5stq1a6dNmzZpzpw5+uSTT+Tq6nrT/Xr37q0ff/xRzZo1U0BAgM26xYsXq379+urQoYMaNWokwzC0bt06ubu737KWefPm6dFHH9WwYcNUvXp1DRkyRKmpqZKk8uXLa9u2bcrIyFDbtm0VGhqqyMhI+fn5WQPWjBkz1Lx5c3Xq1EmtWrVS06ZNVb9+/bvsIQAA4Ewsxs0mScEhUlJS5OfnJ/+Rq+Ti6e3ocgAAuSjRq9ftN0LeikrOk8Pe+P1OTk6Wr6/vLbd1+MgXAADAvYTwBQAAYCLCFwAAgIkIXwAAACbKtw9Zvdftn9L2thP2AAAFTd5M9kbBwsgXAACAiQhfAAAAJiJ8AQAAmIjwBQAAYCLCFwAAgIkIXwAAACYifAEAAJiI8AUAAGAiwhcAAICJCF8AAAAmInwBAACYiPAFAABgIsIXAACAiQhfAAAAJiJ8AQAAmIjwBQAAYCLCFwAAgIkIXwAAACYifAEAAJiI8AUAAGAiwhcAAICJCF8AAAAmcnN0Ache6OQv5eLp7egyACDPJHr1cnQJuBdFJTu6Aka+AAAAzET4AgAAMBHhCwAAwESELwAAABMRvgAAAExE+AIAADDRPRu+YmJiZLFYdOHCBUeXAgAA7iFOH762b98uV1dXtWvXztGlAAAAOH/4WrRokZ555hnFxsYqKSnJ0eUAAIB7nFOHr9TUVK1atUpPPfWUOnTooOjo6Ftu//HHH6tWrVry9PRU5cqVNXPmTJv1lStX1tSpUzVw4EAVKVJEAQEBevfdd222+fXXX9WjRw8VK1ZMJUqUUOfOnZWYmJjL3wwAABRUTh2+Vq5cqZCQEIWEhKhPnz5avHixDMPIdtvdu3ere/fu6tmzp/bt26eoqChNnDgxS2CbOXOmGjRooD179mjYsGF66qmn9NNPP0mSLl26pJYtW8rHx0dbtmxRbGysfHx81K5dO6Wnp2d73rS0NKWkpNgsAADAeTl1+Fq4cKH69OkjSWrXrp3+/PNPff3119luO2vWLD344IOaOHGigoOD1b9/fw0fPlwzZsyw2e6hhx7SsGHDVLVqVT333HMqWbKkYmJiJEkrVqyQi4uL3n//fdWuXVs1atTQ4sWLlZSUZN3mn6ZNmyY/Pz/r4u/vn2vfHwAA5D9OG74OHTqknTt3qmfPnpIkNzc39ejRQ4sWLcp2+/j4eDVp0sSmrUmTJjpy5IgyMjKsbXXq1LH+bbFYVLZsWZ05c0bS9dGzn3/+WUWKFJGPj498fHxUvHhxXblyRUePHs32vOPHj1dycrJ1OX78+F19bwAAkL+5ObqAvLJw4UJdu3ZNFSpUsLYZhiF3d3edP38+y/aGYchisWRp+yd3d3ebzxaLRZmZmZKkzMxM1a9fX8uWLcuyX6lSpbKt09PTU56enrf/QgAAwCk4Zfi6du2ali5dqpkzZ6pNmzY267p166Zly5YpNDTUpr1mzZqKjY21adu+fbuCg4Pl6up6R+cNDw/XypUrVbp0afn6+t7dlwAAAE7JKS87fv755zp//rwGDRqk0NBQm+XRRx/VwoULs+wzZswYff3113r55Zd1+PBhLVmyRG+//bbGjh17x+ft3bu3SpYsqc6dO2vr1q1KSEjQ5s2bFRkZqRMnTuTmVwQAAAWUU4avhQsXqlWrVvLz88uyrlu3boqLi9MPP/xg0x4eHq5Vq1ZpxYoVCg0N1aRJk/TSSy+pf//+d3xeb29vbdmyRQEBAeratatq1KihgQMH6vLly4yEAQAASZLFuNmzF+AQKSkp1+96HLlKLp7eji4HAPJMolcvR5eAe1FUcp4c9sbvd3Jy8m0HXJxy5AsAACC/InwBAACYiPAFAABgIsIXAACAiQhfAAAAJnLKh6w6g/1T2vJ4CgBOLm/uOgPyO0a+AAAATET4AgAAMBHhCwAAwESELwAAABMRvgAAAExE+AIAADAR4QsAAMBEhC8AAAATEb4AAABMRPgCAAAwEeELAADARIQvAAAAExG+AAAATET4AgAAMBHhCwAAwESELwAAABMRvgAAAExE+AIAADAR4QsAAMBEhC8AAAATEb4AAABMRPgCAAAwkZujC0D2Qid/KRdPb0eXAeA2Er16OboEADkRlezoChj5AgAAMBPhCwAAwESELwAAABMRvgAAAExE+AIAADCR04WvmJgYWSwWXbhwwdGlSJIsFovWrl3r6DIAAEA+UWDD1/bt2+Xq6qp27do5uhQAAIA7VmDD16JFi/TMM88oNjZWSUlJDq3l6tWrDj0/AAAoOApk+EpNTdWqVav01FNPqUOHDoqOjs6yzbZt2xQWFiYvLy81bNhQ+/bts6775Zdf1LFjRxUrVkyFCxdWrVq1tG7dOklSdHS0ihYtanOstWvXymKxWD9HRUWpbt26WrRokYKCguTp6SnDMHTkyBE1b95cXl5eqlmzpjZu3Jgn3x8AABRcBfIJ9ytXrlRISIhCQkLUp08fPfPMM5o4caJNQBo3bpxmz56tsmXL6oUXXlCnTp10+PBhubu76+mnn1Z6erq2bNmiwoUL6+DBg/Lx8clRDT///LNWrVqljz/+WK6ursrMzFTXrl1VsmRJ7dixQykpKRo5cuRtj5OWlqa0tDTr55SUlBzVAQAACpYCGb4WLlyoPn36SJLatWunP//8U19//bVatWpl3Wby5Mlq3bq1JGnJkiWqWLGi1qxZo+7duyspKUndunVT7dq1JUlBQUE5riE9PV0ffPCBSpUqJUnasGGD4uPjlZiYqIoVK0qSpk6dqvbt29/yONOmTdOUKVNyfH4AAFAwFbjLjocOHdLOnTvVs2dPSZKbm5t69OihRYsW2WzXqFEj69/FixdXSEiI4uPjJUkjRozQK6+8oiZNmmjy5Mnau3dvjuuoVKmSNXhJUnx8vAICAqzB65813Mz48eOVnJxsXY4fP57jWgAAQMFR4Ea+Fi5cqGvXrqlChQrWNsMw5O7urvPnz99y3xuXJQcPHqy2bdvqf//7nzZs2KBp06Zp5syZeuaZZ+Ti4iLDMGz2y25CfeHChW0+/3Ofv5/vVjw9PeXp6Xnb7QAAgHMoUCNf165d09KlSzVz5kzFxcVZlx9//FGVKlXSsmXLrNvu2LHD+vf58+d1+PBhVa9e3drm7++voUOHavXq1RozZozee+89SVKpUqV08eJFpaamWreNi4u7bW01a9ZUUlKSTp48aW379ttv7+brAgAAJ1SgRr4+//xznT9/XoMGDZKfn5/NukcffVQLFy7Uf/7zH0nSSy+9pBIlSqhMmTKaMGGCSpYsqS5dukiSRo4cqfbt2ys4OFjnz5/XN998oxo1akiSGjZsKG9vb73wwgt65plntHPnzmzvpvynVq1aKSQkRH379tXMmTOVkpKiCRMm5Or3BwAABV+BGvlauHChWrVqlSV4SVK3bt0UFxenH374QZL02muvKTIyUvXr19epU6f06aefysPDQ5KUkZGhp59+WjVq1FC7du0UEhKiuXPnSro+P+z//u//tG7dOtWuXVvLly9XVFTUbWtzcXHRmjVrlJaWpvvuu0+DBw/Wq6++mntfHgAAOAWLkd1kJThMSkqK/Pz85D9ylVw8vR1dDoDbSPTq5egSAOREVHKeHPbG73dycrJ8fX1vuW2BGvkCAAAo6AhfAAAAJiJ8AQAAmIjwBQAAYCLCFwAAgIkK1HO+7iX7p7S97d0SAPKDvLlzCoDzYuQLAADARIQvAAAAExG+AAAATET4AgAAMBHhCwAAwESELwAAABMRvgAAAExE+AIAADAR4QsAAMBEPOE+nzEMQ5KUkpLi4EoAAMCduvG7feN3/FYIX/nM2bNnJUn+/v4OrgQAAOTUxYsX5efnd8ttCF/5TPHixSVJSUlJt/0PD7krJSVF/v7+On78OO/VNBH97jj0vWPQ746Tl31vGIYuXryo8uXL33Zbwlc+4+JyfRqen58f/0/pIL6+vvS9A9DvjkPfOwb97jh51fd3OmjChHsAAAATEb4AAABMRPjKZzw9PTV58mR5eno6upR7Dn3vGPS749D3jkG/O05+6XuLcSf3RAIAACBXMPIFAABgIsIXAACAiQhfAAAAJiJ8AQAAmIjw5QBz585VYGCgvLy8VL9+fW3duvWW22/evFn169eXl5eXgoKCNH/+fJMqdT456fvVq1erdevWKlWqlHx9fdWoUSN9+eWXJlbrPHL6b/6Gbdu2yc3NTXXr1s3bAp1YTvs+LS1NEyZMUKVKleTp6akqVapo0aJFJlXrPHLa78uWLVNYWJi8vb1Vrlw5DRgwwPq6OdyZLVu2qGPHjipfvrwsFovWrl17230c9vtqwFQrVqww3N3djffee884ePCgERkZaRQuXNj45Zdfst3+2LFjhre3txEZGWkcPHjQeO+99wx3d3fjo48+Mrnygi+nfR8ZGWm8/vrrxs6dO43Dhw8b48ePN9zd3Y0ffvjB5MoLtpz2+w0XLlwwgoKCjDZt2hhhYWHmFOtk7On7Tp06GQ0bNjQ2btxoJCQkGN99952xbds2E6su+HLa71u3bjVcXFyM2bNnG8eOHTO2bt1q1KpVy+jSpYvJlRds69atMyZMmGB8/PHHhiRjzZo1t9zekb+vhC+T3XfffcbQoUNt2qpXr248//zz2W7/7LPPGtWrV7dpe/LJJ437778/z2p0Vjnt++zUrFnTmDJlSm6X5tTs7fcePXoYL774ojF58mTCl51y2vdffPGF4efnZ5w9e9aM8pxWTvt9xowZRlBQkE3bnDlzjIoVK+ZZjc7uTsKXI39fuexoovT0dO3evVtt2rSxaW/Tpo22b9+e7T7ffvttlu3btm2rXbt26erVq3lWq7Oxp+//KTMzUxcvXrS+/By3Z2+/L168WEePHtXkyZPzukSnZU/ff/rpp2rQoIGmT5+uChUqKDg4WGPHjtXly5fNKNkp2NPvjRs31okTJ7Ru3ToZhqHffvtNH330kR5++GEzSr5nOfL3lRdrm+iPP/5QRkaGypQpY9NepkwZnT59Ott9Tp8+ne32165d0x9//KFy5crlWb3OxJ6+/6eZM2cqNTVV3bt3z4sSnZI9/X7kyBE9//zz2rp1q9zc+K8oe9nT98eOHVNsbKy8vLy0Zs0a/fHHHxo2bJjOnTvHvK87ZE+/N27cWMuWLVOPHj105coVXbt2TZ06ddJbb71lRsn3LEf+vjLy5QAWi8Xms2EYWdput3127bi9nPb9DcuXL1dUVJRWrlyp0qVL51V5TutO+z0jI0O9evXSlClTFBwcbFZ5Ti0n/+YzMzNlsVi0bNky3XfffXrooYc0a9YsRUdHM/qVQznp94MHD2rEiBGaNGmSdu/erfXr1yshIUFDhw41o9R7mqN+X/mflSYqWbKkXF1ds/yvnzNnzmRJ3zeULVs22+3d3NxUokSJPKvV2djT9zesXLlSgwYN0n//+1+1atUqL8t0Ojnt94sXL2rXrl3as2ePhg8fLul6IDAMQ25ubtqwYYMeeOABU2ov6Oz5N1+uXDlVqFBBfn5+1rYaNWrIMAydOHFC1apVy9OanYE9/T5t2jQ1adJE48aNkyTVqVNHhQsXVrNmzfTKK69whSOPOPL3lZEvE3l4eKh+/frauHGjTfvGjRvVuHHjbPdp1KhRlu03bNigBg0ayN3dPc9qdTb29L10fcSrf//++vDDD5l/YYec9ruvr6/27dunuLg46zJ06FCFhIQoLi5ODRs2NKv0As+ef/NNmjTRyZMn9eeff1rbDh8+LBcXF1WsWDFP63UW9vT7pUuX5OJi+3Ps6uoq6a+RGOQ+h/6+5vmUfti4cQvywoULjYMHDxojR440ChcubCQmJhqGYRjPP/+88fjjj1u3v3Er7KhRo4yDBw8aCxcu5FETdspp33/44YeGm5ub8c477xinTp2yLhcuXHDUVyiQctrv/8TdjvbLad9fvHjRqFixovHoo48aBw4cMDZv3mxUq1bNGDx4sKO+QoGU035fvHix4ebmZsydO9c4evSoERsbazRo0MC47777HPUVCqSLFy8ae/bsMfbs2WNIMmbNmmXs2bPH+oiP/PT7SvhygHfeeceoVKmS4eHhYYSHhxubN2+2ruvXr58RERFhs31MTIxRr149w8PDw6hcubIxb948kyt2Hjnp+4iICENSlqVfv37mF17A5fTf/N8Rvu5OTvs+Pj7eaNWqlVGoUCGjYsWKxujRo41Lly6ZXHXBl9N+nzNnjlGzZk2jUKFCRrly5YzevXsbJ06cMLnqgm3Tpk23/O/s/PT7ajEMxjQBAADMwpwvAAAAExG+AAAATET4AgAAMBHhCwAAwESELwAAABMRvgAAAExE+AIAADAR4QsAAMBEhC8AAAATEb4AAABMRPgCAAAwEeELAADARP8PrS49m/pM1ycAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tab.plot(kind='barh', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RNMKCdBuomyU"
   },
   "source": [
    "### c. Continuous Numerical vs Discrete Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8gdiwIEdombk",
    "outputId": "10d9e571-2c94-44e0-ea29-997898d568ce"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Year_Birth', 'Education', 'Marital_Status', 'Income', 'Kidhome',\n",
       "       'Teenhome', 'Dt_Customer', 'Recency', 'MntWines', 'MntFruits',\n",
       "       'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts',\n",
       "       'MntGoldProds', 'NumDealsPurchases', 'NumWebPurchases',\n",
       "       'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth',\n",
       "       'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1',\n",
       "       'AcceptedCmp2', 'Response', 'Complain', 'Country'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qiJbboYKoMH3",
    "outputId": "45c287f4-6313-41cb-fb44-99102a18fee0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2199 entries, 0 to 2239\n",
      "Data columns (total 28 columns):\n",
      " #   Column               Non-Null Count  Dtype         \n",
      "---  ------               --------------  -----         \n",
      " 0   ID                   2199 non-null   object        \n",
      " 1   Year_Birth           2199 non-null   object        \n",
      " 2   Education            2199 non-null   object        \n",
      " 3   Marital_Status       2199 non-null   object        \n",
      " 4   Income               2199 non-null   float64       \n",
      " 5   Kidhome              2199 non-null   object        \n",
      " 6   Teenhome             2199 non-null   object        \n",
      " 7   Dt_Customer          2199 non-null   datetime64[ns]\n",
      " 8   Recency              2199 non-null   int64         \n",
      " 9   MntWines             2199 non-null   int64         \n",
      " 10  MntFruits            2199 non-null   int64         \n",
      " 11  MntMeatProducts      2199 non-null   int64         \n",
      " 12  MntFishProducts      2199 non-null   int64         \n",
      " 13  MntSweetProducts     2199 non-null   int64         \n",
      " 14  MntGoldProds         2199 non-null   int64         \n",
      " 15  NumDealsPurchases    2199 non-null   object        \n",
      " 16  NumWebPurchases      2199 non-null   object        \n",
      " 17  NumCatalogPurchases  2199 non-null   object        \n",
      " 18  NumStorePurchases    2199 non-null   object        \n",
      " 19  NumWebVisitsMonth    2199 non-null   object        \n",
      " 20  AcceptedCmp3         2199 non-null   object        \n",
      " 21  AcceptedCmp4         2199 non-null   object        \n",
      " 22  AcceptedCmp5         2199 non-null   object        \n",
      " 23  AcceptedCmp1         2199 non-null   object        \n",
      " 24  AcceptedCmp2         2199 non-null   object        \n",
      " 25  Response             2199 non-null   object        \n",
      " 26  Complain             2199 non-null   object        \n",
      " 27  Country              2199 non-null   object        \n",
      "dtypes: datetime64[ns](1), float64(1), int64(7), object(19)\n",
      "memory usage: 562.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 331
    },
    "id": "BSg4OrK_ovoP",
    "outputId": "b152b25c-209b-44bf-ce93-e2cf04cc92cd"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marital_Status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Absurd</th>\n",
       "      <td>65487.0</td>\n",
       "      <td>79244.0</td>\n",
       "      <td>72365.500000</td>\n",
       "      <td>72365.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alone</th>\n",
       "      <td>34176.0</td>\n",
       "      <td>61331.0</td>\n",
       "      <td>43789.000000</td>\n",
       "      <td>35860.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Divorced</th>\n",
       "      <td>1730.0</td>\n",
       "      <td>90687.0</td>\n",
       "      <td>52177.934783</td>\n",
       "      <td>52183.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Married</th>\n",
       "      <td>4023.0</td>\n",
       "      <td>96547.0</td>\n",
       "      <td>51462.983568</td>\n",
       "      <td>51763.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single</th>\n",
       "      <td>3502.0</td>\n",
       "      <td>98777.0</td>\n",
       "      <td>50940.903640</td>\n",
       "      <td>48904.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Together</th>\n",
       "      <td>5648.0</td>\n",
       "      <td>96876.0</td>\n",
       "      <td>51425.252205</td>\n",
       "      <td>51195.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Widow</th>\n",
       "      <td>22123.0</td>\n",
       "      <td>85620.0</td>\n",
       "      <td>56481.552632</td>\n",
       "      <td>56551.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOLO</th>\n",
       "      <td>48432.0</td>\n",
       "      <td>48432.0</td>\n",
       "      <td>48432.000000</td>\n",
       "      <td>48432.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    min      max          mean   median\n",
       "Marital_Status                                         \n",
       "Absurd          65487.0  79244.0  72365.500000  72365.5\n",
       "Alone           34176.0  61331.0  43789.000000  35860.0\n",
       "Divorced         1730.0  90687.0  52177.934783  52183.0\n",
       "Married          4023.0  96547.0  51462.983568  51763.5\n",
       "Single           3502.0  98777.0  50940.903640  48904.0\n",
       "Together         5648.0  96876.0  51425.252205  51195.0\n",
       "Widow           22123.0  85620.0  56481.552632  56551.0\n",
       "YOLO            48432.0  48432.0  48432.000000  48432.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group = df.groupby('Marital_Status')\n",
    "\n",
    "group['Income'].agg(['min', 'max', 'mean', 'median'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 495
    },
    "id": "mhaqLKtBpaf8",
    "outputId": "ff74083b-5795-4a8d-e8d0-92b1dbd650ed"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Income'}, xlabel='Marital_Status'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(by='Marital_Status', column='Income')"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "machine_shape": "hm",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
